Epigenomic induced pluripotent stem cell signatures

ABSTRACT

Provided herein are methods of characterizing the epigenetic signature of human induced pluripotent stem cells. The methods are useful in identifying human induced pluripotent stem cells (hiPSCs), diagnostic markers for incomplete hiPSCs reprogramming, and characterization of the efficacy of different reprogramming techniques.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of PCT Application No. PCT/US2011/058454, filed Oct. 28, 2011 which claims the benefit of U.S. Provisional Application No. 61/407,873, filed Oct. 28, 2010, the contents of which are incorporated herein by reference in their entirety for all purposes.

STATEMENT AS TO RIGHTS TO INVENTIONS MADE UNDER FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under U01ES017166, NSF 0726408, and DK062434 awarded by the National Institutes of Health. The Government has certain rights in the invention.

REFERENCE TO A “SEQUENCE LISTING,” A TABLE, OR A COMPUTER PROGRAM LISTING APPENDIX SUBMITTED AS AN ASCII TEXT FILE

The Sequence Listing written in file 92150-824012_ST25.TXT, created on Apr. 29, 2013, 28,376 bytes, machine format IBM-PC, MS-Windows operating system, is hereby incorporated by reference.

BACKGROUND OF THE INVENTION

Generation of iPSCs from somatic cells offers tremendous potential for therapeutics, the study of disease states, and elucidation of developmental processes (Soldner, F. et al. Cell 136:964-977 (2009); Yamanaka, S. Cell 137:13-17 (2009)). iPSC production techniques introduce active genes that are necessary for pluripotency, or their derivative RNA or protein products, into a somatic cell to induce pluripotent cellular properties that closely resemble those of embryonic stem cells (ESCs) (Takahashi, K. et al., Cell 126:663-676 (2006); Takahashi, K. et al. Cell 131:861-872 (2007); Yu, J. et al. Science 318:1917-1920 (2007); Park, I. et al. Nature 451:141-146 (2008); Yu, J. et al. Science 324:797-801 (2009); Zhao, X. Y. et al. Nature 461:86-90 (2009)). Indeed, iPSCs have been used to produce viable and fertile adult mice, demonstrating their pluripotent potential to form all adult somatic and germline cell types (Zhao, X. Y. et al. Nature 461:86-90 (2009); Boland, M. J. et al. Nature 461:91-94 (2009)).

Fundamentally, the reprogramming process by which a somatic cell acquires pluripotent potential is not a genetic transformation, but an epigenetic one, where the term epigenetic is used to refer to molecular modifications and interactions that impact upon the cellular readout of the genome, such as covalent modifications of DNA and histones, and protein DNA-interactions.

Optimal reprogramming of somatic cells to a pluripotent state requires complete reversion of the somatic epigenome into an ESC-like state, but to date a comprehensive survey of the changes in such epigenetic marks in a variety of independent iPSC lines has not been reported. Therefore, there is a need in the art to understand the epigenomic and methylation characteristics of induced pluripotent stem cells.

Accordingly, Applicants have performed the first whole-genome profiling of the DNA methylomes of multiple ESC, iPSC, and somatic progenitor lines, encompassing reprogramming performed in different laboratories, using different iPSC-inducing technologies, and cells derived from distinct germ layers. This comprehensive base-resolution epigenomic profiling shows that while on a global scale ESC and iPSC methylomes are very similar, iPSC lines display significant reprogramming variability compared to ESCs, including both somatic “memory” and aberrant reprogramming of DNA methylation. Furthermore, all iPSC lines share numerous aberrantly methylated, non-randomly distributed, megabase-scale genic and non-genic regions that Applicants have termed non-CG mega-DMRs. In iPSCs these regions display incomplete or inappropriate reprogramming of the pluripotency-specific non-CG methylation, and are associated with localized differences in CG methylation and transcriptional abnormalities at genes associated with neural development and function.

BRIEF SUMMARY OF THE INVENTION

The methods provided herein are based, inter alia, on the discovery that human induced pluripotent stem cells possess epigenomic signatures relative to human embryonic stem cell. The methods and DMRs provided herein are useful in identifying hiPSCs, diagnostic markers for incomplete hiPSC reprogramming, characterization of the efficacy of different reprogramming techniques, and potential propagation of altered methylation states in derivative differentiated cells.

In one aspect, provided herein is a method of identifying a human induced pluripotent stem cell (hiPSC). The method includes identifying a non-CpG hypomethylated DMR within the human induced pluripotent stem cell. The human induced pluripotent stem cell may be an incompletely reprogrammed hiPSC.

In another aspect, provided herein is a method of identifying a human induced pluripotent stem cell (hiPSC). The method includes identifying a hypomethylated CG-DMR or a hypermethylated CG-DMR within the human induced pluripotent stem cell.

In another aspect, a method of identifying a human induced pluripotent stem cell (hiPSC) is provided. The method includes identifying one or more of a hypomethylated CG-DMR, one or more or a hypermethylated CG-DMR or one or more of a non CpG hypomethylated DMR within the human induced pluripotent stem cell.

In another aspect, a method of identifying a human induced pluripotent stem cell is provided. The method includes determining a methylation pattern of at least a portion of a subject cell and comparing the methylation pattern to a human embryonic stem cell methylation pattern. A difference in methylation pattern is indicative of the subject cell being a human induced pluripotent stem cell. The human induced pluripotent stem cell may be an incompletely reprogrammed induced pluripotent stem cell.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1. Global trends of human iPSC and ESC DNA methylomes. (FIG. 1 a) Percent of all cytosines on each strand of the human genome assayed for each sample. The percent of all sequencing base calls that were methylated (C, resistant to bisulfite conversion) at covered C bases in the (FIG. 1 b) CG and (FIG. 1 c) CH contexts (where H=A, C, or T) throughout the genome, minus the bisulfite non-conversion frequency. For each group of the histogram in FIG. 1A-1C the entries depicted from left to right are somatic, iPSC, ESC and in vitro from pluripotent cell, respectively. (FIG. 1 d) AnnoJ data browser representation of the restoration of non-CG methylation in all iPSC and ESC lines. (FIG. 1 e) Dendrogram of the analyzed cell lines based on Pearson correlation of mCG or mCH levels in 1 kb windows throughout the genome.

FIG. 2. Partially methylated domains (PMDs) become highly methylated upon induction of pluripotency. (FIG. 2 a) Total length of PMDs identified in each cell line, and overlap of PMDs identified in the four somatic cell types. (FIG. 2 b) mRNA-Seq RPKM for all RefSeq genes outside PMDs, and all RefSeq genes within genomic regions defined as PMDs. For ADS-iPS and H1 the ADS PMD genomic regions were used as PMDs. P-value is from two-tailed Wilcoxon test between ADS-PMD and ADS-iPSC-PMD. Abbreviations: PMD, partially methylated domain.

FIG. 3. CG-DMRs identified between pluripotent cells. (FIG. 3 a) Complete linkage hierarchical clustering of mCG density within CG-DMRs identified between all ESC and iPSC DNA methylomes. Each CG DMR was profiled over 20 equally sized bins. (FIG. 3 b) The CG-DMRs for each iPSC line with respect to H1 and H9 ESCs were categorized as having methylation patterns like the progenitor somatic cell line (memory) or iPSC-specific (iDMR). (FIG. 3 c) Number of iPSC hypomethylated and hypermethylated CG-DMRs aberrant in the indicated number of iPSC lines. (FIG. 3 d) Number of all CG-DMRs coincident with indicated genomic and genic features. Abbreviations: DMR, differentially methylated region; CGI, CG Island; TES, transcriptional end site; TSS, transcriptional start site.

FIG. 4. Characterization of CG-DMRs in iPSCs. (FIG. 4 a) Normalized mCG levels (lower y-axis) and normalized H3K27me3 ChIP-Seq read density (upper y-axis) over CG-DMRs hypermethylated in all iPSC lines and flanking genomic regions. (FIG. 4 b) Data browser representation of mRNA, DNA methylation and H3K27me3 density for a CG-DMR identified in all iPSC lines. (FIG. 4 c) Complete linkage hierarchical clustering of mCG density within the CG-DMRs hypomethylated in both FF-iPSC 19.11 and FF-iPSC 19.11-BMP4 relative to H1, H9 and H1-BMP4. Each CG-DMR was profiled over 20 equally sized bins. (FIG. 4 d) same as c for hypermethylated CG-DMRs. (FIG. 4 e) FF-iPSC 19.11 CG-DMR transmission through differentiation to trophoblast cells. CG-DMRs were categorized by methylation state relative to the ESCs (hyper: hypermethylated, hypo: hypomethylated), similarity to somatic progenitor methylation (memory: like progenitor, iDMR: unlike progenitor), and whether the CG-DMR was present in FF-iPSC 19.11 differentiated into trophoblast cells with BMP4 (transmitted) or not (not transmitted).

FIG. 5. Failure to restore megabase-scale regions of non-CG methylation is a hallmark of iPSC reprogramming. (FIG. 5 a) Chromosome ideograms and length distribution (inset) of the 22 ADS-iPSC non-CG mega-DMRs. Blue circles and lines indicate location of individual DMRs. Dark ellipses indicate the location of centromeres. (FIG. 5 b) Normalized mCH levels over all non-CG mega-DMRs and flanking genomic regions. (FIG. 5 c) Lower y-axis as in (FIG. 5 b) for the cell lines indicated. Upper y-axis: normalized H3K9me3 ChIP-Seq read density throughout the non-CG mega-DMRs and flanking genomic regions. Dashed arrows indicate the inverse relationship between mCH and H3K9me3. (FIG. 5 d) Plot displays normalized mCG levels over the non-CG mega-DMRs and flanking genomic regions. Inset is a data browser representation of DNA methylation where vertical bar height indicates mC level, at the 5′ of a non-CG mega-DMR and PMD. (FIG. 5 e) Normalized mCH levels over a non-CG mega-DMR on chromosome 22 and flanking regions. Top panel shows gene models and ADS-iPSC mCH. (FIG. 5 f) Comparison of transcript abundance between H1 and ADS-iPSC. Each dot represents a RefSeq gene within the 22 non-CG mega-DMRs. Red dots indicate genes that have a CG-DMR within 2 kb of the transcriptional start site. Dark dots indicate genes that have a CG-DMR within 2 kb of the transcriptional start site, are hypermethylated in all iPSC lines and are associated with loss of H3K27me3. Dashed lines represent 2-fold difference. (FIG. 5 g) The number of genes with a given transcript abundance ratio between H1 and ADS-iPSC for all RefSeq genes within the non-CG mega-DMRs.

FIG. 6. Global similarity in transcriptional and DNA methylation patterns between ESCs and iPSCs. (FIG. 6 a) Complete linkage hierarchical clustering of both biological replicates of ADS-adipose somatic cells, ADS cekks, ADS-iPSCs and H1 ESCs, base don RPKM values from strand-specific RNA-seq. (FIG. 6 b) Plot of the density of mCH identified in chromosome 10. Lines represent smooting of mCH density in 10 kb windows. Black arrow indicates a large region of dissimilarity between ESCs and iPSCs. Abbreviations: mCG/mCH, methylated cytosine in the CG/CH context.

FIG. 7. Density of DNA methylation at NANOG binding sites. The average relative DNA methylation densities in each sequence context are shown from 1.5 kb upstream to 1.5 kb downstream of the predicted sites of DNA-protein interaction of NANOG identified by ChIP-seq in H1 embryonic stem cells. Shaded areas indicate the 95^(th) confidence interval for the mean.

FIG. 8. Density of DNA methylation at OCT4 binding sites. The average relative DNA methylation densities in each sequence context are shown from 1.5 kb upstream to 1.5 kb downstream of the predicted sites of DNA-protein interaction of OCT4 identified by ChIP-seq in H1 embryonic stem cells. Shaded areas indicate the 95^(th) confidence interval for the mean.

FIG. 9. Density of DNA methylation at SOX2 binding sites. The average relative DNA methylation densities in each sequence context are shown from 1.5 kb upstream to 1.5 kb downstream of the predicted sites of DNA-protein interaction of SOX2 identified by ChIP-seq in H1 embryonic stem cells. Shaded areas indicate the 95^(th) confidence interval for the mean.

FIG. 10. Density of DNA methylation at enhancer sites. The average relative DNA methylation densities in each sequence context in 100 bp windows are displayed throughout 5 kb upstream to 5 kb downstream of enhancers identified in H1 embryonic stem cells. Shaded areas indicate the 95^(th) confidence interval for the mean.

FIG. 11. DNA methylation patterns at pluripotency-related genes. AnnoJ data browser representation of DNA methylation in all cell lines at pluripotency-related genes.

FIG. 12. Restoration of fully-methylated state in iPSCs at somatic PMDs. AnnoJ browser representation of DNA methylation at a PMD genomic region that is partially methylated in somatic cell types, but fully methylated in both ESCs and iPSCs. For the DNA methylation tracks, vertical lines above and below the dotted central line indicate the presence of methylcytosines on the Watson and Crick strands, respectively. Only DNA methylation sites in the CG context are displayed, and the vertical height of the line indicates the methylation level of each methylsytosine. Abbreviations: MCG, methylcytosine (CG context); PMD partially methylated domain.

FIG. 13. Clustering of CG-DMRs at CGIs hypomethylated in ESCs relative to somatic cells. Complete linkage hierarchical clustering of mCG density within CG-DMRs identified between all ESCs and somatic cells, hypomethylated in ESCs relative to somatic cells, and coincident with CGIs. Each CG-DMR was profiled over 20 equally sized bins.

FIG. 14. Clustering of CG-DMRs at CGIs hypomethylated in somatic cells relative to ESCs. Complete linkage hierarchical clustering of mCG density within CG-DMRs identified between all ESCs and somatic cells, hypomethylated in somatic cells relative to ESCs, and coincident with CGIs. Each CG-DMR was profiled over 20 equally sized bins.

FIG. 15. Heatmaps of MCG density for the CG-DMRs in each iPSC line that are similar or dissimilar to their progenitor somatice lines. mCG density is profiled in 20 bins over the CG_DMRs aberrant in each iPSC line (left black box in each heatmap) in respect to both H1 and H9 hESCs. The CG-DMRs are divided according to the similarity to the methylation statures of the progenitor somatic lines (right black blox in each heatmap): CG-DMRs similar to progenitor somatic line (memory) in left column, and CG-DMRs dissimilar to parental line on the right column (iDMR).

FIG. 16. Analysis of over-represented motifs in the CG-DMRs conserved among all iPSC lines. 11 hypermethylated and 119 hypomethylated CG-DMRs shared by all five iPSC lines were considered, and sequences 1 kb upstream and downstream of the center of each DMR were retrieved (Watson strand). MEME was used to identify sequence patterns that occur repeatedly in the two groups of DNA sequences. As a control, since the CG-DMRs are highly enriched for CG islands, a set of 2 kb sequences were retrieved from 100 random CG islands. The top 5 sequences within each group were considered (hypermethylated and hypomethylated CG-DMRs and random CG islands; E-value<0.01). These sequences were analyzed with TOMTOM searching for transcription factor binding sites (TFBS, based on the JASPAR core DB; q-value, 0.5). Three out of the five sequences identified within the set of hypermethylated CG-DMRs were found to contain binding sites that not found in the reference CG island set. The logo blots for these three sequences are displayed, with a length of 41, 15 and 48 nt, together with the logo plot of the matching TFBS. All analysis were performed with the MEME suite.

FIG. 17. Clustering of FF-iPSC 19.11 CG-DMRs relative to ESCs, where somatic DNA methylation memory in FF-iPSC 19.11 is transmitted through cellular differentiation. Complete linkage hierarchical clustering of mCG density within CG-DMRs identified between FF-iPSC 19.11 and both ESCs, where FF-iPSC 19.11, FF somatic progenitor, and FF-iPSC 19.11 differentiated with BMP4 to trophoblast lineage display the same methylation state. Separate heatmaps are shown for iPSC hypermethylation and hypomethylation relative to the ESCs. Each CG-DMR was profiled over 20 equally sized bins.

FIG. 18. Clustering of FF-iPSC 19.11 CG-DMRs relative to ESCs, where FF-iPSC 19.11 methylation patterns unlike both the FF somatic progenitor and ESCs are transmitted through cellular differentiation. Complete lineage hierarchical clustering of mCG density within CG-DMRs identified between FF-iPSC 19.11 and both ESCs, where FF-iPSC 19.11 shows a methylation state not found in either ESCs or FF somatic progenitor, and FF-iPSC 19.11 differentiated with BMP4 totrophoblast displays the same methylation state as FF-iPSC 19.11. Separate heatmaps are shown for iPSC hypermethylation and hypomethylation relative to the ESCs. Each CG-DMR was profiled over 20 equally sized bins.

FIG. 19. Validation of the CG-DMRs in two additional ESC lines. (FIG. 19 a) Complete linkage hierarchical clustering of mCG density within the 1175 CG-DMRs identified between iPSCs and H1 and H9 ESCs (FIG. 3 a), which were also profiled in the HSF1 and H9-Laurent ESCs. (FIG. 19 b) as in FIG. 19 a for the hypermethylated or hypomethylated CG-DMRs in all iPSCs. In both FIG. 19 a and FIG. 19 b the two additional ESCs show high similarity to H1 and H9 ESCs.

FIG. 20. non-CG mega-DMRs. (FIG. 22 a) Chromosome ideograms of the 7 large non-CG DMRs where H1 is hypomethylated relative to ADS-iPSC. (FIG. 22 b), Normalized mCH levels over the 7 large non-CG DMRs and flanking genomic regions where H1 is hypomethylated relative to ADS-iPSC. (FIG. 22 c) Chromosome ideograms of all 78 non-CG DMRs where ADS-iPSC is hypomethylated relative to H1. (FIG. 22 d) Normalized mCH levels over all 78 non-CG DMRs where ADS-iPSC is hypomethylated relative to H1 and flanking genomic regions. (FIG. 22 e) Lower y-axis as in (f) for the cell lines indicated. Upper y-axis shows profiles of normalized H3K9me3 ChIP-Seq read density throughout the 78 non-CG DMRs. (FIG. 22 g) Plot displays normalized mCG levels over the 78 non-CG DMRs and flanking genomic regions.

FIG. 21. Retroviral insertion sites in ADS-iPSC. Ideogram of the retroviral insertion sites in the genome of ADS-iPSC, indicated by drak dots.

FIG. 22. Validation of the non-CG mega-DMRs in two additional ESC lines. Normalized mCH levels over all non-CG mega-DMRs and flanking genomic regions, including the previously published HSF1 and H9-Laurent ESCs.

DETAILED DESCRIPTION OF THE INVENTION I. DEFINITIONS

The following definitions are provided to facilitate understanding of certain terms used frequently herein and are not meant to limit the scope of the present disclosure.

“Nucleic acid” refers to deoxyribonucleotides or ribonucleotides and polymers thereof in either single- or double-stranded form, and complements thereof.

The words “complementary” or “complementarity” refer to the ability of a nucleic acid in a polynucleotide to form a base pair with another nucleic acid in a second polynucleotide. For example, the sequence A-G-T is complementary to the sequence T-C-A. Complementarity may be partial, in which only some of the nucleic acids match according to base pairing, or complete, where all the nucleic acids match according to base pairing.

The terms “identical” or percent “identity,” in the context of two or more nucleic acids, refer to two or more sequences or subsequences that are the same or have a specified percentage of nucleotides that are the same (i.e., about 60% identity, preferably 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or higher identity over a specified region, when compared and aligned for maximum correspondence over a comparison window or designated region) as measured using a BLAST or BLAST 2.0 sequence comparison algorithms with default parameters described below, or by manual alignment and visual inspection (see, e.g., the NCBI web site or the like). Such sequences are then said to be “substantially identical.” This definition also refers to, or may be applied to, the compliment of a test sequence. The definition also includes sequences that have deletions and/or additions, as well as those that have substitutions. As described below, the preferred algorithms can account for gaps and the like. Preferably, identity exists over a region that is at least about 25 amino acids or nucleotides in length, or more preferably over a region that is 50-100 amino acids or nucleotides in length.

The phrase “stringent hybridization conditions” refers to conditions under which a probe will hybridize to its target sequence, typically in a complex mixture of nucleic acids, but to not other sequences. Stringent conditions are sequence-dependent and will be different in different circumstances. Longer sequences hybridize specifically at higher temperatures. An extensive guide to the hybridization of nucleic acids is found in Tijssen, Techniques in Biochemistry and Molecular Biology—Hybridization with Nucleic Probes, “Overview of principles of hybridization and the strategy of nucleic acid assays” (1993). Generally, stringent conditions are selected to be about 5-10° C. lower than the thermal melting point (Tm) for the specific sequence at a defined ionic strength pH. The Tm is the temperature (under defined ionic strength, pH, and nucleic concentration) at which 50% of the probes complementary to the target hybridize to the target sequence at equilibrium (as the target sequences are present in excess, at Tm, 50% of the probes are occupied at equilibrium). Stringent conditions may also be achieved with the addition of destabilizing agents such as formamide. For selective or specific hybridization, a positive signal is at least two times background, preferably 10 times background hybridization. Exemplary stringent hybridization conditions can be as following: 50% formamide, 5×SSC, and 1% SDS, incubating at 42° C., or, 5×SSC, 1% SDS, incubating at 65° C., with wash in 0.2×SSC, and 0.1% SDS at 65° C.

A variety of methods of specific DNA and RNA measurement that use nucleic acid hybridization techniques are known to those of skill in the art (see, Sambrook, supra). Some methods involve electrophoretic separation (e.g., Southern blot for detecting DNA, and Northern blot for detecting RNA), but measurement of DNA and RNA can also be carried out in the absence of electrophoretic separation (e.g., by dot blot).

The sensitivity of the hybridization assays may be enhanced through use of a nucleic acid amplification system that multiplies the target nucleic acid being detected. Examples of such systems include the polymerase chain reaction (PCR) system and the ligase chain reaction (LCR) system. Other methods recently described in the art are the nucleic acid sequence based amplification (NASBA, Cangene, Mississauga, Ontario) and Q Beta Replicase systems. These systems can be used to directly identify mutants where the PCR or LCR primers are designed to be extended or ligated only when a selected sequence is present. Alternatively, the selected sequences can be generally amplified using, for example, nonspecific PCR primers and the amplified target region later probed for a specific sequence indicative of a mutation. It is understood that various detection probes, including Taqman® and molecular beacon probes can be used to monitor amplification reaction products, e.g., in real time.

The word “polynucleotide” refers to a linear sequence of nucleotides. The nucleotides can be ribonucleotides, deoxyribonucleotides, or a mixture of both. Examples of polynucleotides contemplated herein include single and double stranded DNA, single and double stranded RNA (including miRNA), and hybrid molecules having mixtures of single and double stranded DNA and RNA.

The words “protein”, “peptide”, and “polypeptide” are used interchangeably to denote an amino acid polymer or a set of two or more interacting or bound amino acid polymers.

The term “gene” means the segment of DNA involved in producing a protein; it includes regions preceding and following the coding region (leader and trailer) as well as intervening sequences (introns) between individual coding segments (exons). The leader, the trailer as well as the introns include regulatory elements that are necessary during the transcription and the translation of a gene. Further, a “protein gene product” is a protein expressed from a particular gene.

A “viral vector” is a viral-derived nucleic acid that is capable of transporting another nucleic acid into a cell. A viral vector is capable of directing expression of a protein or proteins encoded by one or more genes carried by the vector when it is present in the appropriate environment. Examples for viral vectors include, but are not limited to retroviral, adenoviral, lentiviral and adeno-associated viral vectors.

The term “transfection” or “transfecting” is defined as a process of introducing nucleic acid molecules to a cell by non-viral or viral-based methods. Non-viral methods of transfection include any appropriate transfection method that does not use viral DNA or viral particles as a delivery system to introduce the nucleic acid molecule into the cell. Exemplary non-viral transfection methods include calcium phosphate transfection, liposomal transfection, nucleofection, sonoporation, transfection through heat shock, magnetifection and electroporation. For viral-based methods of transfection any useful viral vector may be used in the methods described herein. Examples for viral vectors include, but are not limited to retroviral, adenoviral, lentiviral and adeno-associated viral vectors.

The word “expression” or “expressed” as used herein in reference to a gene means the transcriptional and/or translational product of that gene. The level of expression of a DNA molecule in a cell may be determined on the basis of either the amount of corresponding mRNA that is present within the cell or the amount of protein encoded by that DNA produced by the cell (Sambrook et al., 1989 Molecular Cloning: A Laboratory Manual, 18.1-18.88).

The term “plasmid” refers to a nucleic acid molecule that encodes for genes and/or regulatory elements necessary for the expression of genes. Expression of a gene from a plasmid can occur in cis or in trans. If a gene is expressed in cis, the gene and the regulatory elements are encoded by the same plasmid. Expression in trans refers to the instance where the gene and the regulatory elements are encoded by separate plasmids.

The term “episomal” refers to the extra-chromosomal state of a plasmid in a cell. Episomal plasmids are nucleic acid molecules that are not part of the chromosomal DNA and replicate independently thereof.

A “cell culture” is a population of cells residing outside of an organism. These cells are optionally primary cells isolated from a cell bank, animal, or blood bank, or secondary cells that are derived from one of these sources and have been immortalized for long-lived in vitro cultures.

A “stem cell” is a cell characterized by the ability of self-renewal through mitotic cell division and the potential to differentiate into a tissue or an organ. Among mammalian stem cells, embryonic and adult stem cells can be distinguished. Embryonic stem cells reside in the blastocyst and give rise to embryonic tissues, whereas adult stem cells reside in adult tissues for the purpose of tissue regeneration and repair.

The term “pluripotent” or “pluripotency” refers to cells with the ability to give rise to progeny that can undergo differentiation, under appropriate conditions, into cell types that collectively exhibit characteristics associated with cell lineages from the three germ layers (endoderm, mesoderm, and ectoderm). Pluripotent stem cells can contribute to tissues of a prenatal, postnatal or adult organism. A standard art-accepted test, such as the ability to form a teratoma in 8-12 week old SCID mice, can be used to establish the pluripotency of a cell population. However, identification of various pluripotent stem cell characteristics can also be used to identify pluripotent cells.

“Pluripotent stem cell characteristics” refer to characteristics of a cell that distinguish pluripotent stem cells from other cells. Expression or non-expression of certain combinations of molecular markers are examples of characteristics of pluripotent stem cells. More specifically, human pluripotent stem cells may express at least some, and optionally all, of the markers from the following non-limiting list: SSEA-3, SSEA-4, TRA-1-60, TRA-1-81, TRA-2-49/6E, ALP, Sox2, E-cadherin, UTF-1, Oct4, Lin28, Rex1, and Nanog. Cell morphologies associated with pluripotent stem cells are also pluripotent stem cell characteristics.

An “induced pluripotent stem cell” refers to a pluripotent stem cell artificially derived from a non-pluripotent cell. A non-pluripotent cell can be a cell of lesser potency to self-renew and differentiate than a pluripotent stem cell. Cells of lesser potency can be, but are not limited to, somatic stem cells, tissue specific progenitor cells, primary or secondary cells. Without limitation, a somatic stem cell can be a hematopoietic stem cell, a mesenchymal stem cell, an epithelial stem cell, a skin stem cell or a neural stem cell. A tissue specific progenitor refers to a cell devoid of self-renewal potential that is committed to differentiate into a specific organ or tissue. A primary cell includes any cell of an adult or fetal organism apart from egg cells, sperm cells and stem cells. Examples of useful primary cells include, but are not limited to, skin cells, bone cells, blood cells, cells of internal organs and cells of connective tissue. A secondary cell is derived from a primary cell and has been immortalized for long-lived in vitro cell culture.

The term “reprogramming” refers to the process of dedifferentiating a non-pluripotent cell (e.g. an origin cell) into a cell exhibiting pluripotent stem cell characteristics (e.g. a human induced pluripotent stem cell).

The terms “CG” or “CpG” can be used interchangeably and refer to regions of a DNA molecule where a cytosine nucleotide occurs next to a guanine nucleotide in the linear sequence of bases (linear strand) within the DNA molecule. Nucleotides forming a linear strand in a DNA molecule are linked through a phosphate. Therefore, a CG site is also referred to as a “CpG” site, a shorthand for cytosine-phosphate-guanine The “CpG” notation is further used to distinguish the linear sequence of cytosine and guanine from the CG base-pairing of cytosine and guanine, where cytosine and guanine are located on opposite strands of a DNA molecule. Cytosines in CpG dinucleotides can be methylated to form 5-methylcytosine. In mammals, methylating the cytosine within a gene may turn the gene off. Enzymes that add a methyl group to a cytosine within a DNA molecule are referred to as DNA methyltransferases.

A “non-CpG hypomethylated DMR,” as used herein, refers to a differentially methylated region (DMR) of an iPSC genome having a greater number of non-methylated non-CpG sites relative to the corresponding region of a human embryonic stem cell. The non-CpG hypomethylated DMR is typically about 100 kb to 4000 kb in length (e.g. 100 to 3000 kb or 100 to 2000 kb).

A non-CpG site is a nucleotide methylation site in which the nucleotide does not form part of a CG sequence.

A “hypomethylated CG-DMR,” as used herein, refers to a differentially methylated region (DMR) of an iPSC genome having a greater number of non-methylated CpG sites relative to the corresponding region of a human embryonic stem cell. The hypomethylated CG DMR is typically about 100 to 4000 kb in length (e.g. 100 to 3000 kb or 100 to 2000 kb). A CpG cite is a nucleotide methylation cite in which the nucleotide forms part of a CG sequence.

A “hypermethylated CG-DMR,” as used herein, refers to a differentially methylated region (DMR) of an iPSC genome having a greater number of methylated CpG sites relative to the corresponding region of a human embryonic stem cell. The hypermethylated CG DMR is typically about 100 to 4000 kb in length (e.g. 100 to 3000 kb or 100 to 2000 kb).

The term “hypermethylated promoter,” as used herein, refers to a promoter region of an iPSC genome within or coextensive with a non-CpG hypomethylated DMR having a greater number of methylated sites relative to the corresponding region of a human embryonic stem cell.

The origin cell is typically a partially differentiated or fully differentiated human cell. Methods of reprogramming partially differentiated or fully differentiated human cells are well known in the art (e.g. using one or more of the Yamanaka reprogramming factors).

II. METHODS

In one aspect, provided herein is a method of identifying a human induced pluripotent stem cell (hiPSC). The method includes identifying a non-CpG hypomethylated DMR within the human induced pluripotent stem cell. The human induced pluripotent stem cell may be an incompletely reprogrammed hiPSC.

In some embodiments, the non-CpG hypomethylated DMR is characterized by decreased methylation relative to a corresponding non-CpG DMR of a human embryonic stem cell. The comparison may be performed using the criteria outlined below in the Example section entitled “Non-CG mega-DMRs” (Example 6; and see also FIG. 5). In some embodiments, the non-CpG hypomethylated DMR is one or more regions identified in Table 3A and/or Table 3B. Non limiting examples of chromosomal regions that are a non-CpG hypomethylated DMR include the nucleotide sequence of chromosome 7 from position 156,535,825 to position 158,080,000, the nucleotide sequence of chromosome 8 from position 2,161,971 to position 4,761,970, the nucleotide sequence of chromosome 10 from position 131,888,467 to position 133,321,763, or the nucleotide sequence of chromosome 22 from position 46,357,370 to position 48,540,808.

In some embodiments, the non-CpG hypomethylated DMR includes from about 100 kb to about 5000 kb. In other embodiments, the non-CpG hypomethylated DMR includes from about 200 kb to about 5000 kb, 300 kb to about 5000 kb, from about 400 kb to about 5000 kb, 500 kb to about 5000 kb, from about 600 kb to about 5000 kb, 700 kb to about 5000 kb, from about 800 kb to about 5000 kb, 900 kb to about 5000 kb, from about 1000 kb to about 5000 kb, 1100 kb to about 5000 kb, from about 1200 kb to about 5000 kb, 1300 kb to about 5000 kb, from about 1400 kb to about 5000 kb, 1500 kb to about 5000 kb, from about 1600 kb to about 5000 kb, 1700 kb to about 5000 kb, from about 1800 kb to about 5000 kb, 1900 kb to about 5000 kb, from about 2000 kb to about 5000 kb, 2100 kb to about 5000 kb, from about 2200 kb to about 5000 kb, 2300 kb to about 5000 kb, from about 2400 kb to about 5000 kb, 2500 kb to about 5000 kb, from about 2600 kb to about 5000 kb, 2700 kb to about 5000 kb, from about 2800 kb to about 5000 kb, 2900 kb to about 5000 kb, from about 3000 kb to about 5000 kb, 3100 kb to about 5000 kb, from about 3200 kb to about 5000 kb, 3300 kb to about 5000 kb, from about 3400 kb to about 5000 kb, 3500 kb to about 5000 kb, from about 3600 kb to about 5000 kb, 3700 kb to about 5000 kb, from about 3800 kb to about 5000 kb, 3900 kb to about 5000 kb, from about 4000 kb to about 5000 kb, 4100 kb to about 5000 kb, from about 4200 kb to about 5000 kb, 4300 kb to about 5000 kb, from about 4400 kb to about 5000 kb, 4500 kb to about 5000 kb, from about 4600 kb to about 5000 kb, 4700 kb to about 5000 kb, from about 4800 kb to about 5000 kb, or 4900 kb to about 5000 kb. In some embodiments, the non-CpG hypomethylated DMR includes 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800, 2900, 3000, 4100, 4200, 4300, 4400, 4500, 4600, 4700, 4800, 4900, or 5000 kb.

In some embodiments, the non-CpG hypomethylated DMR is proximal to a telomere or centromere. The term “proximal to a telomere or centromere,” as used herein in reference to a non-CpG hypomethylated DMR, means within about 10%, preferably about 5%, of chromosomal length from a telomere or centromere. In some embodiments, the term refers to the non-CpG hypomethylated DMR being within less than 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8% or 9%. In some embodiments, the non-CpG hypomethylated DMR is within about 10% of chromosomal length from a telomere or centromere. In other embodiments, the non-CpG hypomethylated DMR is within about 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2,%, 1%, or 0.5% of chromosomal length from a telomere or centromere.

The non-CpG hypomethylated DMR may alternatively or additionally include one or more hypermethylated promoters (e.g. transcriptional start sites). The hypermethylated promoters are promoters within the hiPSC genome that are methylated at a level less (e.g. on average) than the level of methylation of the corresponding promoter of an embryonic stem cell. The hypermethylated promoter may be one or more of the regions identified in Table 4. In Table 4 the regions included in a hypermethylated promoter are identified by a sequence reference number (i.e. RefSeq; e.g. NM_(—)020828). A person of skill in the art would immediately recognize that each sequence reference number is a reference to a nucleotide sequence listed in the publicly available data base of the National Center for Biotechnology Information (NCBI). Therefore, the sequence reference number is a sequence identifier for a nucleotide sequences included in the hypermethylated promoters provided herein. In some embodiments, the hypermethylated promoters include the nucleotide sequence of SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5 or SEQ ID NO:6.

In certain embodiments, the non-CpG hypomethylated DMR includes one or more transcriptionally repressed genes. The transcriptionally repressed genes are genes within the hiPSC genome that are transcribed at a level less (e.g. on average) than the level of transcription of the corresponding genes of an embryonic stem cell.

The non-CpG hypomethylated DMR may alternatively or additionally substantially overlap with a partially methylated domain of an origin cell of the human induced pluripotent stem cell. Where the non-CpG hypomethylated DMR substantially overlaps with a partially methylated domain of an origin cell of the human induced pluripotent stem cell, the non-CpG hypomethylated DMR of the iPSC overlaps with a domain that was partially methylated in the origin cell prior to reprogramming the origin cell to a iPSC. In some embodiments, the partially methylated domain of an origin cell is hypomethylated. In other embodiments, the partially methylated domain of an origin cell is hypermethylated. In other embodiments, the non-CpG hypomethylated DMR is the partially methylated domain of an origin cell of the human induced pluripotent stem cell. Where the non-CpG hypomethylated DMR substantially overlaps with a partially methylated domain of an origin cell of the human induced pluripotent stem cell, the non-CpG hypomethylated DMR includes at least 10% of the partially methylated domain of an origin cell. In some embodiments, the non-CpG hypomethylated DMR includes between 10% to 100% of the partially methylated domain of an origin cell. In other embodiments, the non-CpG hypomethylated DMR includes between 20% to 100%, 25% to 100%, 30% to 100%, 35% to 100%, 40% to 100%, 45% to 100%, 50% to 100%, 55% to 100%, 60% to 100%, 65% to 100%, 70% to 100%, 75% to 100%, 80% to 100%, 85% to 100%, 90% to 100%, or 95% to 100% of the partially methylated domain of an origin cell. An “origin cell” refers to the cell from which the hiPSC is derived (e.g. reprogrammed). Thus, origin cells are non-pluripotent cells, which are either partially or completely differentiated.

In some embodiments, the non-CpG hypomethylated DMR is proximal to a telomere or centromere, includes one or more hypermethylated promoters, includes one or more transcriptionally repressed genes or substantially overlaps with a partially methylated domain of an origin cell of the human induced pluripotent stem cell. In other embodiments, the non-CpG hypomethylated DMR is proximal to a telomere or centromere, include one or more hypermethylated promoters, includes one or more transcriptionally repressed genes and substantially overlaps with a partially methylated domain of an origin cell of the human induced pluripotent stem cell.

In some embodiments, the non-CpG hypomethylated DMR is spatially concordant with a H3K9me3 heterochromatin modification. The term “H3K9me3” refers to a histone 3 having three methyl groups covalently attached to the lysine at postion 9. H3K9me3 is a histone modification characteristic of heterochromatin (i.e. transcriptionally repressed chromatin). The term “spatially concordant” means the H3K9me3 heterochromatin modification is sufficiently proximal to the non-CpG hypomethylated DMR to result in a functional change within the non-CpG hypomethylated DMR. FIG. 5 depicts examples of H3K9me3 heterochromatin modifications included in non-CpG hypomethylated DMRs.

The method may further include identifying one or more CG-DMRs within the hiPSC. A CG DMR is typically about 100 to 4000 kb in length. In some embodiments, the CG-DMR includes from about 100 kb to about 5000 kb. In other embodiments, the CG-DMR includes from about 200 kb to about 5000 kb, 300 kb to about 5000 kb, from about 400 kb to about 5000 kb, 500 kb to about 5000 kb, from about 600 kb to about 5000 kb, 700 kb to about 5000 kb, from about 800 kb to about 5000 kb, 900 kb to about 5000 kb, from about 1000 kb to about 5000 kb, 1100 kb to about 5000 kb, from about 1200 kb to about 5000 kb, 1300 kb to about 5000 kb, from about 1400 kb to about 5000 kb, 1500 kb to about 5000 kb, from about 1600 kb to about 5000 kb, 1700 kb to about 5000 kb, from about 1800 kb to about 5000 kb, 1900 kb to about 5000 kb, from about 2000 kb to about 5000 kb, 2100 kb to about 5000 kb, from about 2200 kb to about 5000 kb, 2300 kb to about 5000 kb, from about 2400 kb to about 5000 kb, 2500 kb to about 5000 kb, from about 2600 kb to about 5000 kb, 2700 kb to about 5000 kb, from about 2800 kb to about 5000 kb, 2900 kb to about 5000 kb, from about 3000 kb to about 5000 kb, 3100 kb to about 5000 kb, from about 3200 kb to about 5000 kb, 3300 kb to about 5000 kb, from about 3400 kb to about 5000 kb, 3500 kb to about 5000 kb, from about 3600 kb to about 5000 kb, 3700 kb to about 5000 kb, from about 3800 kb to about 5000 kb, 3900 kb to about 5000 kb, from about 4000 kb to about 5000 kb, 4100 kb to about 5000 kb, from about 4200 kb to about 5000 kb, 4300 kb to about 5000 kb, from about 4400 kb to about 5000 kb, 4500 kb to about 5000 kb, from about 4600 kb to about 5000 kb, 4700 kb to about 5000 kb, from about 4800 kb to about 5000 kb, or 4900 kb to about 5000 kb. In some embodiments, the CG-DMR includes 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800, 2900, 3000, 4100, 4200, 4300, 4400, 4500, 4600, 4700, 4800, 4900, or 5000 kb. The CG-DMR may be a hypomethylated CG-DMR or a hypermethylated CG-DMR. In some embodiments, the CG-DMR is hypomethylated. The hypomethylated CG-DMR is characterized by decreased methylation relative to the methylation of a corresponding CG-DMR in a human embryonic stem cell. In some embodiments, the CG-DMR is hypermethylated. The hypermethylated CG-DMR is characterized by increased methylation relative to the methylation of a corresponding CG-DMR in a human embryonic stem cell. The comparison may be performed using the criteria outlined in the Examples section entitled “CG-DMRs” (Example 6; and see also FIG. 3). In some embodiments, the hypomethylated CG-DMR is not the nucleotide sequence of chromosome 2 starting at nucleotide position 87,905,315 ending at nucleotide position 87,906,414 and having a length of 1099 bp. Non-limiting examples of hypomethylated CG-DMRs and hypermethylated CG-DMRs are provided in Tables 1 and 2, respectively. For example, the hypermethylated CG-DMR is the nucleotide sequence of chromosome 12 starting at nucleotide position 125,033,730 ending at nucleotide position 125,034,829 and having a length of 1099 bp. The chromosomes as referred to herein are human chromosomes listed under human genome annotation 18 (i.e. hg18 annotation). Therefore, the sequence for each chromosome disclosed herein can be identified by accessing the public UCSC Human Browser Gateway database under hg18 annotation or NCBI36/hg18. A person of ordinary skill in the art will immediately be able to identify the individual nucleotide sequences provided herein by accessing the UCSC Human Browser Gateway database.

In some embodiments of the aspects above, the identifying is indicative of an aberrantly reprogrammed human induced pluripotent stem cell. An aberrantly reprogrammed human induced pluripotent stem cell is a cell that after the process of dedifferentiation still exhibits characteristics of a non-pluripotent cell (e.g. an origin cell) and lacks certain characteristics of a pluripotent cell. The aberrantly reprogrammed human induced pluripotent stem cell may be an incompletely reprogrammed human hiPSC.

The reprogrammed hiPSC may be formed by sexual or asexual propagation of one or more parent reprogrammed human induced pluripotent stem cell.

In another aspect, provided herein is a method of identifying a human induced pluripotent stem cell (hiPSC). The method includes identifying a hypomethylated CG-DMR or a hypermethylated CG-DMR within the human induced pluripotent stem cell. The human induced pluripotent stem cell may be an incompletely reprogrammed hiPSC. In some embodiments, the hypomethylated CG-DMR is characterized by decreased methylation relative to the methylation of a corresponding CG-DMR of a human embryonic stem cell. The hypermethylated CG-DMR is characterized by increased methylation relative to the methylation of a corresponding CG-DMR of a human embryonic stem cell. The comparison may be performed using the criteria outlined in the Examples section entitled “CG-DMRs” (Example 5; and see also FIG. 3). In some embodiments, the hypomethylated CG-DMR is not the nucleotide sequence of chromosome 2 starting at nucleotide position 87,905,315 ending at nucleotide position 87,906,414 and having a length of 1099 bp. Non-limiting examples of hypomethylated CG-DMRs and hypermethylated CG-DMRs are provided in Tables 1 and 2, respectively. For example, the hypermethylated CG-DMR is the nucleotide sequence of chromosome 12 starting at nucleotide position 125,033,730 ending at nucleotide position 125,034,829 and having a length of 1099 bp. The chromosomes as referred to herein are human chromosomes listed under human genome annotation 18 (i.e. hg18 annotation). Therefore, the sequence for each chromosome disclosed herein can be identified by accessing the public UCSC Human Browser Gateway database under hg18 annotation or NCBI36/hg18. A person of ordinary skill in the art will immediately be able to identify the individual nucleotide sequences provided herein by accessing the UCSC Human Browser Gateway database.

In some embodiments, the method further includes identifying one or more non-CpG hypomethylated DMR(s). In some embodiments, the non-CpG hypomethylated DMR is one or more regions identified in Table 3A and/or Table 3B. Non limiting examples of chromosomal regions that are a non-CpG hypomethylated DMR include the nucleotide sequence of chromosome 7 from position 156,535,825 to position 158,080,000, the nucleotide sequence of chromosome 8 from position 2,161,971 to position 4,761,970, the nucleotide sequence of chromosome 10 from position 131,888,467 to position 133,321,763, or the nucleotide sequence of chromosome 22 from position 46,357,370 to position 48,540,808. The chromosomes as referred to herein are human chromosomes listed under human genome annotation 18 (i.e. hg18 annotation). Therefore, the sequence for each chromosome disclosed herein can be identified by accessing the public UCSC Human Browser Gateway database under hg18 annotation or NCBI36/hg18. A person of ordinary skill in the art will immediately be able to identify the individual nucleotide sequences provided herein by accessing the UCSC Human Browser Gateway database. In some embodiments, the non-CpG hypomethylated DMR is proximal to a telomere or centromere. The non-CpG hypomethylated DMR may alternatively or additionally include one or more hypermethylated promoters (e.g. transcriptional start sites). The hypermethylated promoter may be one or more of the regions identified in Table 4. In Table 4 the regions included in a hypermethylated promoter are identified by a sequence reference number (i.e. RefSeq; e.g. NM_(—)020828). A person of skill in the art would immediately recognize that each sequence reference number is a reference to a nucleotide sequence listed in the publicly available data base of the National Center for Biotechnology Information (NCBI). Therefore, the sequence reference number is a sequence identifier for a nucleotide sequences included in the hypermethylated promoters provided herein. In some embodiments, the hypermethylated promoters include the nucleotide sequence of SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5 or SEQ ID NO:6.

In certain embodiments, the non-CpG hypomethylated DMR includes one or more transcriptionally repressed genes. The transcriptionally repressed genes are genes within the hiPSC genome that are transcribed at a level less (e.g. on average) than the level of transcription of the corresponding genes of an embryonic stem cell. The non-CpG hypomethylated DMR may alternatively or additionally substantially overlap with a partially methylated domain of an origin cell of the human induced pluripotent stem cell. An “origin cell” refers to the cell from which the hiPSC is derived (e.g. reprogrammed). In some embodiments, the non-CpG hypomethylated DMR is spatially concordant with a H3K9me3 heterochromatin modification.

In some embodiments of the aspects above, the identifying is indicative of an aberrantly reprogrammed hiPSC.

The reprogrammed hiPSC may be formed by sexual or asexual propagation of one or more parent reprogrammed human induced pluripotent stem cell.

In another aspect, provided herein is a method of identifying a human induced pluripotent stem cell (hiPSC). The method includes identifying one or more of a hypomethylated CG-DMR, one or more of a hypermethylated CG-DMR or one more of a non-CpG hypomethylated DMR within the human induced pluripotent stem cell. In some embodiments, the method includes identifying one or more of a hypomethylated CG-DMR, one or more of a hypermethylated CG-DMR and one more of a non-CpG hypomethylated DMR within the human induced pluripotent stem cell. In some embodiments, the method includes identifying one or more of a hypomethylated CG-DMR and one or more of a hypermethylated CG-DMR within the human induced pluripotent stem cell. In other embodiments, the method includes identifying one or more of a hypomethylated CG-DMR and one or more of a non-CpG hypomethylated DMR within the human induced pluripotent stem cell. In other embodiments, the method includes identifying one or more of a hypermethylated CG-DMR and one or more of a non-CpG hypomethylated DMR within the human induced pluripotent stem cell. The hypomethylated CG-DMR, a hypermethylated CG-DMR or a non-CpG hypomethylated DMR may be one or more of the regions set forth in Table 1, Table 2, Table 3A, Table 3B and Table 4. The characteristics of the hypomethylated CG-DMR, the hypermethylated CG-DMR and the non-CpG hypomethylated DMR set forth in the aspects above are equally applicable to this aspect.

In another aspect, a method of identifying a human induced pluripotent stem cell is provided. The method includes determining a methylation pattern of at least a portion of a subject cell and comparing the methylation pattern to a human embryonic stem cell methylation pattern. A difference in methylation pattern is indicative of the subject cell being a human induced pluripotent stem cell. The human induced pluripotent stem cell may be an incompletely reprogrammed induced pluripotent stem cell.

In some embodiments, the methylation pattern of the portion of the subject cell includes a non-CpG hypomethylated DMR. In some embodiments, the non-CpG hypomethylated DMR is proximal to a telomere or centromer. In other embodiments, the non-CpG hypomethylated DMR includes one or more hypermethylated promoters. In other embodiments, the non-CpG hypomethylated DMR substantially overlaps with a partially methylated domain of a non-pluripotent cell. In other embodiments, the non-CpG hypomethylated DMR includes one or more transcriptionally repressed genes. In other embodiments, the non-CpG hypomethylated DMR is spatially concordant with a H3K9me3 heterochromatin modification. The characteristics of the non-CpG hypomethylated DMR set forth in the aspects above are equally applicable to this aspect.

In other embodiments, the methylation pattern of the portion of the subject cell includes a CG-DMR within said subject cell. In some embodiments, the CG-DMR is a hypomethylated CG-DMR. In other embodiments, the CG-DMR is a hypermethylated CG-DMR. The characteristics of the CG-DMR set forth in the aspects above are equally applicable to this aspect.

In some embodiments, the methylation pattern of the portion of the subject cell includes a non-CpG hypomethylated DMR and a CG-DMR within the subject cell. In some embodiments, the non-CpG hypomethylated DMR is proximal to a telomere or centromer. In other embodiments, the non-CpG hypomethylated DMR includes one or more hypermethylated promoters. In other embodiments, the non-CpG hypomethylated DMR substantially overlaps with a partially methylated domain of a non-pluripotent cell. In other embodiments, the non-CpG hypomethylated DMR comprises one or more transcriptionally repressed genes. In other embodiments, the non-CpG hypomethylated DMR is spatially concordant with a H3K9me3 heterochromatin modification. In some embodiments, the CG-DMR is a hypomethylated CG-DMR. In other embodiments, the CG-DMR is a hypermethylated CG-DMR.

In other embodiments, the methylation pattern of the portion of the subject cell includes a plurality of non-CpG hypomethylated DMRs and a plurality of CG-DMRs within the subject cell. In some embodiments, the plurality of non-CpG hypomethylated DMRs is proximal to a telomere or centromer. In other embodiments, the plurality of non-CpG hypomethylated DMRs includes one or more hypermethylated promoters. In other embodiments, the plurality of non-CpG hypomethylated DMRs substantially overlaps with a plurality of partially methylated domains of a non-pluripotent cell. In other embodiments, the plurality of non-CpG hypomethylated DMRs comprises one or more transcriptionally repressed genes. In other embodiments, the plurality of non-CpG hypomethylated DMRs is spatially concordant with H3K9me3 heterochromatin modifications. In some embodiments, the plurality of CG-DMR is a plurality of hypomethylated CG-DMRs. In other embodiments, the plurality of CG-DMRs is a plurality of hypermethylated CG-DMRs.

In some embodiments, the difference in methylation pattern is indicative of the subject cell being an aberrantly reprogrammed human induced pluripotent stem cell.

In some embodiments, the subject cell is formed by sexual or asexual propagation of one or more parent reprogrammed human induced pluripotent stem cells.

III. TABLES

TABLE 1 CG-DMRs hypermethylated in every iPSC line with respect to all ESC lines. chromosome start coordinate end coordinate length (base pairs) chr12 125033730 125034829 1099 chr12 128952730 128955529 2799 chr12 129064330 129065329 999 chr12 129092430 129093429 999 chr12 129209230 129215029 5799 chr2 87905315 87906414 1099 chr10 132998500 133001399 2899 chr7 157173784 157180183 6399 chr22 47143596 47145195 1599 chr22 47263296 47266095 2799 chr22 47349296 47351495 2199

TABLE 2 CG-DMRs hypomethylated in every iPSC line with respect to all ESC lines. chromosome start coordinate end coordinate length (base pairs) chr5 43431922 43433621 1699 chr5 139991422 139993021 1599 chr5 157030122 157031721 1599 chr5 178419622 178420621 999 chr5 180033122 180034121 999 chr5 180474422 180475421 999 chr12 12831030 12832029 999 chr12 30213930 30215029 1099 chr12 42438130 42439729 1599 chr12 63070030 63071029 999 chr12 67426130 67427229 1099 chr2 9531315 9533514 2199 chr2 45088415 45089414 999 chr2 85664715 85665714 999 chr2 102233315 102234314 999 chr2 106047815 106049414 1599 chr2 112906715 112907814 1099 chr2 130901715 130902714 999 chr2 175302615 175303614 999 chr2 201688815 201690414 1599 chr2 201691215 201692214 999 chr2 242391015 242393214 2199 chr20 3589078 3590077 999 chr20 28224978 28225977 999 chr20 32320178 32321177 999 chr20 61109578 61110577 999 chr3 8784755 8785754 999 chr3 55491955 55493654 1699 chr3 127159355 127160354 999 chr3 148579955 148581754 1799 chr3 170964955 170966054 1099 chr3 182903755 182905954 2199 chr3 195598255 195600454 2199 chr3 198177955 198179554 1599 chr6 24465653 24466752 1099 chr6 27636953 27637952 999 chr6 27744753 27745752 999 chr6 30001653 30003252 1599 chr6 31383553 31384552 999 chr6 38790453 38791552 1099 chr6 74075453 74077052 1599 chr6 74217453 74219152 1699 chr6 142451253 142452352 1099 chr6 159210253 159211252 999 chr6 161019853 161020852 999 chr6 168178353 168180052 1699 chr1 20542468 20543467 999 chr1 28781168 28782167 999 chr1 35814868 35815867 999 chr1 41122168 41123167 999 chr1 146247368 146248967 1599 chr1 149370768 149372967 2199 chr1 153531268 153532267 999 chr1 205884868 205885867 999 chr18 50776889 50777888 999 chr19 7640922 7642021 1099 chr19 12166322 12167321 999 chr19 13789122 13790121 999 chr19 19512322 19513321 999 chr19 40087422 40088421 999 chr19 40938222 40940421 2199 chr19 41603722 41604721 999 chr19 43568322 43569921 1599 chr19 54405522 54406521 999 chr19 58252922 58254021 1099 chr13 35768729 35770328 1599 chr13 42046129 42047828 1699 chr13 42495129 42496128 999 chr10 26541100 26542099 999 chr10 88285200 88286799 1599 chr10 103523500 103524599 1099 chr10 118358100 118359699 1599 chr8 41543706 41544705 999 chr8 104452206 104453805 1599 chr8 105547606 105548605 999 chr16 23673234 23674233 999 chr16 53919734 53923833 4099 chr16 73291534 73292633 1099 chr9 25667166 25668765 1599 chr9 44057866 44059465 1599 chr9 124018866 124019965 1099 chr21 33320749 33324748 3999 chr21 33327349 33328348 999 chr21 41719549 41721148 1599 chr7 1065684 1066683 999 chr7 12117384 12118383 999 chr7 24289984 24291583 1599 chr7 63660384 63661383 999 chr7 63985984 63987583 1599 chr7 94863384 94864383 999 chr7 121729084 121730083 999 chr7 154976384 154977383 999 chr11 14882973 14884572 1599 chr11 15051373 15052972 1599 chr11 20574673 20576872 2199 chr11 87880973 87882572 1599 chr11 92341873 92342872 999 chr14 69107954 69108953 999 chr14 73105454 73107053 1599 chr14 73128054 73129053 999 chr14 96128254 96129853 1599 chr14 101623754 101624753 999 chr15 29520144 29521143 999 chr15 46270644 46271743 1099 chr15 76343644 76344643 999 chr15 87722544 87723543 999 chr4 6297864 6298863 999 chr4 15313564 15314663 1099 chr4 39124964 39126063 1099 chr4 75082564 75083563 999 chr4 104859864 104861463 1599 chr17 23735290 23736289 999 chr17 34128990 34130589 1599 chr17 42756490 42757589 1099 chr17 43973690 43974689 999 chr17 69864190 69865789 1599 chr17 77224990 77225989 999 chr22 30928396 30930595 2199 chr22 49047896 49048895 999

TABLE 3A Non-CG DMRs. Number of iPSC lines in which the mega-DMR is hypo- methylated (iPSC- H1 mCH level/ mCH > ESC-mCH ADS- Chro- level < 0.7) iPSC mo- Length vs vs H1 and mCH some Start End (bp) H1 H9 average  1  4,099,596  5,616,921 1,517,325 4 4  2  4,563,747  4,863,746 299,999 3 1  5  2,241,348  4,916,347 2,674,999 3 3  7 152,535,825 154,331,422 1,795,597 5 4  7 156,920,000 158,080,000 1,160,000 5 5  8  2,161,971  4,761,970 2,599,999 5 4  8 138,614,971 139,764,970 1,149,999 4 4 10 131,888,467 133,321,763 1,433,296 5 4 11  49,075,540  49,509,006 433,466 4 2 12 124,710,587 129,485,586 4,774,999 4 3 15  18,702,374  19,470,373 767,999 4 3 16  5,572,321  7,447,320 1,874,999 4 3 16  7,950,000  8,550,000 600,000 4 3 17  10,700,552  11,750,000 1,049,448 3 3 17  21,459,552  21,762,057 302,505 1 1 19  21,499,279  21,701,289 202,010 3 1 19  61,638,290  61,788,289 149,999 2 2 20  39,879,269  41,504,268 1,624,999 4 4 20  52,720,000  53,804,268 1,084,268 5 3 21  40,220,292  41,420,291 1,199,999 4 4 22  15,615,370  15,790,369 174,999 3 2 22  46,357,370  48,540,808 2,183,438 5 5

TABLE 3B Non-CG DMRs. Number of iPSC lines in which the mega-DMR is hypermethylated (ESC-mCH level/ ADS- iPSC-mCH iPSC Chro- level < 0.7) mCH > mo- Length vs vs H1 and H1 mCH some Start End (bp) H1 H9 average  1  49,378,922  50,178,921 799,999 3 3  1 150,437,596 151,071,921 634,325 2 3  8  48,040,201  48,260,970 220,769 4 4 10  37,302,467  37,715,763 413,296 5 5 18  14,177,036  14,939,475 762,439 4 4 19  48,008,279  48,397,289 389,010 2 3 21  14,120,292  14,279,213 158,921 5 5

TABLE 4 Hypermethylated Promoter Regions. Hypermethylated Hypermethylated TSS in TSS in all RefSeq Gene symbol H1 RPKM ADS-iPS RPKM ADS-iPSC iPSC lines NM_018836 AJAP1 0.449 0.15 + − NM_001134222 IRX2 22.1 2.44 + − NM_033267 IRX2 18.8 2.43 + − NM_178569 C5orf38 1.6 0.134 + − NM_024337 IRX1 2.58 0.57 + − NM_001039350 DPP6 0.362 0.001 − − NM_130797 DPP6 0.00279 0.001 + − NM_001936 DPP6 0.457 0.001 + − NM_130843 PTPRN2 0.871 0.147 − − NM_002847 PTPRN2 0.416 0.0667 − − NM_015912 FAM135B 0.0524 0.00187 + − NM_174937 TCERG1L 1.28 0.0481 + + NM_001136103 TMEM132C 0.14 0.001 − − NM_145648 SLC15A4 5.18 2.31 + − NM_144669 GLT1D1 2.08 0.0544 + − NM_133448 TMEM132D 1.94 0.0253 + + NM_007197 FZD10 0.138 0.0113 + + NM_004764 PIWIL1 0.00903 0.001 − − NM_018723 A2BP1 0.0184 0.00119 + − NM_001142333 A2BP1 0.0204 0.00119 + − NM_145891 A2BP1 0.0704 0.0016 − − NM_001001415 ZNF429 5.58 0.967 − − NM_173531 ZNF100 2.45 0.654 − − NM_022103 ZNF667 4.7 0.843 − − NM_020813 ZNF471 0.745 0.107 − − NM_020828 ZFP28 2.92 0.48 + + NM_001001668 ZNF470 2.62 0.999 + − NM_133170 PTPRT 0.0526 0.00158 + − NM_007050 PTPRT 0.0806 0.00168 + − NM_006198 PCP4 0.603 0.0177 − − NM_175878 XKR3 0.0863 0.001 − − NM_001082967 FAM19A5 3.05 0.001 + + NM_015381 FAM19A5 0.669 0.001 + + NM_004476 FOLH1 0.00192 0.0216 − − NM_001014986 FOLH1 0.00218 0.0249 − − NM_015347 RIMBP2 0.0226 0.106 − − NM_004662 DNAH9 0.001 0.0112 − − NM_001389 DSCAM 0.0145 0.0392 + − RefSeq genes displaying ≧2-fold difference in transcript abundance between H1 and ADS-iPSC, located within non-CG mega-DMRs that are hypomethylated in iPSC lines relative to ESCs. Abbreviations: TSS, transcriptional start site; RPKM, reads per kilobase of transcript per million reads.

IV. EXAMPLES Example 1

Similar Global DNA Methylome Characteristics of ESCs and iPSCs.

In order to assess the degree to which a somatic cell DNA methylome is reprogrammed into an ESC-like state by induction of a pluripotent state, Applicants generated comprehensive, single base resolution DNA methylomes of a range of cell types using the shotgun bisulfite-sequencing method, MethylC-Seq (Lister, R. et al. Nature 462:315-322 (2009). Our central focus was a high-efficiency, feeder-free reprogramming system (Sugii, S. et al. Proceedings of the National Academy of Sciences (2010)), in which female adipose-derived mesenchymal stem cells (ADS) were reprogrammed into a pluripotent state by retroviral transformation with the OCT4, SOX2, KLF4 and c-MYC genes (ADS-iPSC). The ADS-iPSCs expressed pluripotency-related marker genes, differentiated into all three embryonic germ layers in vitro, and were able to form multilineage teratomas (Sugii, S. et al. Proceedings of the National Academy of Sciences (2010)), thereby satisfying the criteria for pluripotency in human cells (Daley, G. et al. Cell Stem Cell 4:200-1; author reply 202 (2009)). Additionally, Applicants analyzed the DNA methylome of adipocytes derived from the ADS cells (ADS-adipose) through adipogenic differentiation conditions. For these cell lines Applicants generated high coverage whole-genome methylomes, using between 549-633 million uniquely mapped, non-clonal, paired-end sequencing reads (71.1-80.6 Gb) to provide an average coverage of 11.5-13.1 X per strand of the human genome, assaying 87.6-94.5% of the cytosines in the genome (FIG. 1 a, Supplementary Table 1). This is equivalent to the methylomes Applicants previously reported (Lister, R. et al. Nature 462:315-322 (2009)) for two human cell lines, H1 human embryonic stem cells (Thomson, J. A. et al. Science 282:1145-1147 (1998)) and IMR90 fetal lung fibroblasts (Nichols, W. W. et al. Science 196:60-63 (1977)) that Applicants have included as ESC and somatic reference methylomes in this study. Furthermore, to explore the variation between independent iPSC lines potentially due to stochastic reprogramming events, parental somatic cell type, reprogramming technique, and laboratory-specific effects, Applicants generated full DNA methylomes for four additional iPSC lines that were isolated in an independent laboratory: an iPSC line generated by lentiviral integration of the OCT4, SOX2, NANOG and LIN28 genes into IMR90 lung fibroblasts (IMR90-iPSC) (Yu, J. et al. Science 318:1917-1920 (2007)), and three independent iPSC lines generated by reprogramming of foreskin fibroblasts by non-integrating episomal vectors (FF-iPSC 6.9, FF-iPSC 19.7, FF-iPSC 19.11), as described previously (Yu, J. et al. Science 324:797-801 (2009)). For each additional iPSC line Applicants generated between 303-358 million uniquely mapped, non-clonal, single-end sequencing reads (25.1-29.5 Gb) to provide an average coverage of 4.1-4.8 X per strand of the human genome, assaying 75.5-81.6% of genomic cytosines (FIG. 1 a, Supplementary Table 1). Browsing the methylomes at single base-resolution can be performed at http://neomorph.salk.edu/ips_methylomes using the AnnoJ browser (www.annoj.org). Together, this constitutes the first unbiased, whole-genome survey of any iPSC methylome at single-base resolution and, accordingly, enables the first analysis of variability in DNA methylation between ESC and iPSC throughout the entire genome.

The genome-wide frequency of DNA methylation at both CG and non-CG (mCH, where H=A, C or T) sites indicated that iPSCs resemble ESCs and are distinct from somatic cells. All ESC and iPSC lines were methylated at CG dinucleotides at a frequency of 81-85%, compared to 63-67% in the somatic cell lines (FIG. 1 b), consistent with the global partially-methylated state previously observed in the IMR90 fibroblast genome (Lister, R. et al. Nature 462:315-322 (2009)). Similarly, while somatic cells contained negligible levels of cytosine methylation in the non-CG context, all pluripotent cells harbored significant mCH at a similar frequency (FIG. 1 c). As observed in ESCs (Lister, R. et al. Nature 462:315-322 (2009)), all iPSC genomes displayed enrichment for mCH in genes (FIG. 1 d). Clustering of cell lines based on either mCG or mCH levels in 10 kb windows of the genome demonstrated that on a genome-scale the DNA methylomes of ESCs and iPSCs are similar and highly distinct from somatic cells, including an adult stem cell (FIG. 1 e). This relationship agrees with clustering of cell types based on transcriptional activity, as assayed by strand-specific RNA-Seq on the H1, ADS, ADS-adipose and ADS-iPSC lines (Supplementary FIG. 1). The smoothed profile of mCH density in 10 kb windows showed that on the chromosomal level the density profile of non-CG methylation in iPSCs is highly similar to ESCs (FIG. 1 f), with the notable exception of some large dissimilar regions (FIG. 1 f, arrow). Analysis of DNA methylation patterns flanking enhancers and OCT4, NANOG and SOX2 protein-DNA interaction sites identified in H1 ESCs revealed that the depletion of DNA methylation at these sites in iPSC genomes and the H9 ESC genome was consistent with the depletion previously observed for H1 ESCs (Supplementary FIG. 2-5). Furthermore, very similar mCG patterns at the pluripotency-related genes OCT4/POU5F1, NANOG, UTF1 and ZFP42 were observed in all ESCs and iPSCs (Supplementary FIG. 6). Taken together, these data indicate that, on the genome-scale and at these crucial pluripotency genes, iPSC and ESC DNA methylomes closely resemble one another. Moreover, the re-methylation of the genome in the non-CG context through reprogramming of somatic cells confirms earlier suggestions that global non-CG methylation is a hallmark of the pluripotent cell state (Lister, R. et al. Nature 462:315-322 (2009)).

Applicants previously discovered that over 40% of the genome of IMR90 fibroblasts was in a partially methylated state, with large regions of each autosome displaying lower average levels of CG methylation, termed Partially Methylated Domains (PMDs) (Lister, R. et al. Nature 462:315-322 (2009)). The PMDs were frequently associated with the heterochromatin modification H3K27me3 and lower transcript abundance of genes within the PMDs (Lister, R. et al. Nature 462:315-322 (2009)) which may indicate that the differentiated cell (IMR90) no longer requires, or is unable to maintain, high mCG levels in these regions. The DNA methylomes of the two non-pluripotent cell types Applicants have profiled here, ADS and ADS-adipose, also contain PMDs in a similar proportion of the genome to IMR90 (FIG. 2 a). Moreover, the regions of the genome that are partially methylated between IMR90 and ADS are highly concordant, with 74% and 87% of the PMD bases overlapping for IMR90 and ADS, respectively (FIG. 2 a). Notably, the PMDs in the ADS and IMR90 genomes were transformed to a fully methylated state in the CG context by induction of a pluripotent state (Supplementary FIG. 7); the PMD state was effectively absent from the pluripotent cell genomes (FIG. 2 a). As previously observed in IMR90, the transcript abundance associated with genes located within PMDs was lower than the average for all other genes (FIG. 2 b). Notably, for the genes located in ADS PMD regions, transcript abundance was over 1 order of magnitude higher in ADS-iPSC cells than ADS (Wilcoxon P=3.7e⁻¹³⁹), indicating that reprogramming is able to reverse the transcriptional repression associated with the PMD state, returning transcript abundance to similar levels observed in the H1 ESCs.

Example 2

Somatic Cell Memory and Aberrant Reprogramming of CG DNA Methylation.

DNA methylation proximal to promoters and transcriptional start sites is generally associated with lower gene expression, and distinct cell types display abundant variable methylation patterns proximal to genes that may influence transcriptional activity (Lister, R. et al. Nature 462:315-322 (2009); Rakyan, V. K. et al. Genome Res. 18:1518-1529 (2008); Laurent, L. et al. Genome Research (2010)). A central question in somatic cell reprogramming is the efficacy and variability of resetting to an ESC-like state the somatic DNA methylation configurations that may affect gene activity. Although global patterns of DNA methylation in the CG context appeared very similar between ESCs and iPSCs (FIG. 1-2), a comprehensive analysis of CG DNA methylation between all ESC and iPSC lines identified 1175 differentially methylated regions (CG-DMRs) that were differentially methylated in at least one iPSC or ESC line (1% FDR, Supplementary Table 2) and in total comprised 1.68 Mb ranging from 1 to 11 kb in length. Importantly, identification of CG-DMRs between the H1 and H9 ESCs with the same criteria provided no results. A heatmap of the mCG density in the 1175 CG-DMRs allowed visualization of the variability within and between ESC and iPSC lines (FIG. 3). While mCG patterns within each category of cells (ESC, iPSC, somatic) was generally consistent and distinct from the cells in each other category, individual cell lines displayed some variability. Notably, for a subset of CG-DMRs (for example, regions indicated by the blue line in FIG. 3), ADS-iPSC was generally hypomethylated relative to the other iPSC lines, a pattern that was similarly observed between the ADS and IMR90 parental somatic cells, potentially indicating memory of the parental somatic cell DNA methylation patterns. A recent study reported the retention of somatic cell DNA methylation patterns in early-passage (4) mouse iPSCs that was sufficient to distinguish between iPSC lines derived from different progenitor cell types, and which was subsequently attenuated after further passages (10-16 total) (Polo, J. M. et al. Nature biotechnology (2010)). However, the iPSCs analyzed here included relatively late passage iPSC lines (15-66 passages, Supplementary Table 1), indicating that Applicants are able to discriminate somatic DNA methylation patterns in iPSCs that are resistant to resetting to an ESC-like state.

DNA methylation at CG islands (CGIs) proximal to gene promoters and transcriptional start sites is inhibitory to transcriptional activity (Cedar, H. & Bergman, Nat Rev Genet 10:295-304 (2009). While demethylation of promoters that lack CGIs upon reprogramming to a pluripotent state is well established, for example at OCT4/POUF51 and NANOG (Mikkelsen, T. S. et al. Nature 454:49-55 (2008)), it is unknown whether highly methylated CGIs in differentiated cells can be demethylated during iPSC reprogramming. To address this issue Applicants analyzed CG-DMRs between the ESCs and somatic cells (1% FDR) that overlapped with CGIs, and the methylation state in the iPSCs at these CGIs. Of 2145 CG-DMRs coincident with CGIs (CGI-DMRs), 1337 and 309 were more than 2-fold hypermethylated in ESCs and somatic cells, respectively. Of the 309 CGI-DMRs hypermethylated in somatic cells, 82.5% were hypomethylated in the iPSCs and were similar to ESCs, 7.1% were dissimilar to both ESCs and somatic cells, and 10.4% remained hypermethylated in iPSCs (Supplementary FIG. 8). Of the 1337 CGI-DMRs hypermethylated in ESCs, 71.8% were hypermethylated akin to ESCs in the iPSCs, 13.5% were methylated at an intermediate level between ESCs and somatic cells, and 14.7% remained hypomethylated in iPSCs (Supplementary FIG. 9). Together, these results indicate that CGIs in iPSCs are predominantly reprogrammed to an ESC-like state, and in particular, hypermethylated CGIs are not especially resistant to reprogramming.

Aberrant CG methylation patterns identified between iPSCs and ESCs may be categorized as either failure to reprogram the progenitor somatic cell methylation patterns (memory, like progenitor) or inappropriate methylation found neither in the ESC nor progenitor somatic cells. Comparison of ADS-iPSC CG-DMRs to the ADS progenitor, and IMR90-iPSC CG-DMRs versus the progenitor IMR90 showed that in iPSC lines 57-60% of CG-DMRs were aberrant with respect to ESCs (P=0.01) and reflected the progenitor methylation state (FIG. 4 a). Accordingly, in the iPSC lines, 40-43% of the CG-DMRs could be classified as “unlike progenitor”, and 30-42% of these CG-DMRs were in common between both iPSC lines.

Inspection of the concordance of methylation state in the five iPSC lines showed that 65% of the CG-DMRs were aberrant with respect to the ESCs in at least two iPSC lines, with 19% being confirmed in all five iPSC lines (P=0.01, FIG. 4 b, Supplementary table 3). The majority of CG-DMRs (80%) occurred at CGIs, and to a lesser extent near or within genes (62%), with 29% and 19% located within 2 kb of transcriptional start and end sites, respectively (FIG. 4 c). Closer inspection of the CG-DMRs confirmed in all five iPSC lines revealed that the vast majority of them (203 of 218, or 93%) were hypomethylated in the iPSC lines, suggesting that the general deficiency in resetting DNA methylation patterns during reprogramming is insufficient methylation. Notably, the remaining 15 CG-DMRs hypermethylated in the iPSC lines were also associated with the absence of the heterochromatic H3K27me3 histone modification, whereas the H1 ESC showed marked enrichment of this modification at these sites (FIG. 4 d,e). The combination of these aberrant DNA methylation and histone marks at transcriptional start sites was frequently associated with transcriptional repression in the ADS-iPSC compared to H1 ESCs (FIG. 4 e).

Several conclusions can be made from this catalogue of CG-DMRs. First, reprogramming a somatic cell to a pluripotent state generates hundreds of aberrantly methylated loci, predominantly at CGIs and associated with genes. Second, while insufficient reprogramming manifested as a memory of the progenitor somatic cell methylation state is common, a high incidence of CG-DMRs that were unlike both the progenitor somatic cell and ESCs indicates that aberrant methylation patterns dissimilar to both the start and endpoints of the reprogramming process are frequently generated. Third, while there is variability in the loci that are differentially methylated between iPSC lines, a high proportion of CG-DMRs are found in multiple independent iPSC lines, indicating that these regions have a strong propensity to be insufficiently or aberrantly reprogrammed. Fourth, a core set of CG-DMRs was present in every iPSC line, representing hotspots of aberrant epigenomic reprogramming common to iPSCs.

Example 3

Failure to Restore Megabase-Scale Regions of Non-CG Methylation is a Hallmark of iPSC Reprogramming.

While non-CG DNA methylation levels and distribution were very similar between ESCs and iPSCs on a whole-genome and chromosomal scale (FIG. 1), a systematic comparison of non-CG methylation levels between the H1 and the ADS-iPSC lines throughout the autosomes revealed the presence of 29 large, non-CG differentially methylated regions (FDR=1%, fold difference in mCH level≧2, Supplementary table 4). These non-CG “mega”-DMRs tended to be very large, with half greater than 1 Mb in length, the longest ˜4.8 Mb, and in total all 29 comprised 32.4 Mb, or ˜1% of the genome (FIG. 5 a, inset). The majority of non-CG mega-DMRs were hypomethylated in the mCH context in the ADS-iPSC line (22, total length=29.1 Mb), while only few (7, total length=3.4 Mb) were hypomethylated in H1 relative to ADS-iPSC (Supplementary FIG. 10 a,b). The H1-hypomethylated non-CG mega-DMRs contained 36 genes enriched for biological processes related to epidermal cell differentiation (54% of 36 genes, P=1.5e⁻³⁵), and that predominantly were not expressed in H1 but were transcribed at a low level in ADS-iPSC (Supplementary Table 5). These H1-hypomethylated mega-DMRs encompassed two gene clusters encoding pregnancy-specific beta-1 glycoproteins (chromosome 19) and late cornified envelope proteins within the epidermal differentiation complex (chromosome 1) which are involved in epidermal differentiation during development (Marshall, D. et al., Proc Natl Acad Sci USA 98:13031-13036 (2001)). Focusing subsequent analysis on the 22 non-CG mega-DMRs in which the ADS-iPSC line failed to restore non-CG methylation upon establishment of pluripotency, Applicants discovered that non-CG mega-DMR localization was strongly biased towards close proximity to centromeres and telomeres (FIG. 5 a, 15 of 22 located within 5% of chromosomal length from a telomere or centromere, Poisson P=1e⁻¹²), suggesting that somatic cell reprogramming may be susceptible to DNA methylation abnormalities in these chromosomal regions. Applicants hypothesized that the retroviral insertion used to introduce the pluripotency factors in ADS-iPSC may have disrupted the reprogramming of DNA methylation. However, identification of the retroviral insertion sites by PCR-amplification and Illumina-based sequencing of the junctions between the ADS-iPSC genomic DNA and the MMLV retroviral 5′ and 3′ LTRs did not identify significant overlap between the 17 insertion sites and the non-CG mega-DMRs (Supplementary FIG. 11, Supplementary table 6).

Applicants next profiled the average non-CG DNA methylation level throughout the 22 ADS-iPSC hypomethylated non-CG mega-DMRs and flanking genomic regions for each of the 7 ESC and iPSC methylomes. This analysis revealed that depletion of non-CG methylation in these regions was a common feature of the independent iPSC lines, whereas such depletion was not observed in either of the ESC lines (FIG. 5 b, and FIG. 1 f, arrow). Depletion of non-CG methylation in individual regions was evident in multiple iPSCs, specifically with 20, 15 and 6 of the DMRs manifesting depletion in at least 3, 4 or all 5 iPSC lines, respectively (Supplementary Table 4). Applicants hypothesized that the localized failure to restore non-CG methylation in these large regions could be mechanistically linked to the presence of particular covalent histone modifications that impart a regional chromatin conformation that is refractive to re-methylation at CH sites. Accordingly, Applicants analyzed the genomic distribution of the heterochromatin modifications H3K9me3 and H3K27me3 throughout the genomes of two of the iPSC lines (FF-iPSC 19.11 and IMR90-iPSC (Hawkins, R. D. et al. Cell Stem Cell 6:479-491 (2010)), and in both cell lines Applicants identified significant regional enrichment of H3K9me3 that was spatially concordant with the non-CG mega-DMRs, and absent from the flanking genomic regions (FIG. 5 c). Notably, H3K9me3 was not observed throughout these same genomic regions in H1 ES cells, but rather a low level of regional enrichment was observed at the 5′ end of the DMRs coincident with a small decrease in H1 mCH level (FIG. 5 c). Furthermore, the IMR90 genome also displayed enrichment of H3K9me3 highly spatially correlated with the non-CG mega-DMRs. Additionally, Applicants profiled the average level of CG methylation in the genomes of all analyzed cell lines, revealing that the regions of the genome identified as non-CG mega-DMRs in ADS-iPSCs tend to be partially methylated in non-pluripotent cells (21 of 22 non-CG mega-DMRs overlap with ADS PMDs, accounting for 99.5% of non-CG mega-DMR bases, FIG. 5 d). Taken together, these data indicate that specific large regions of somatic cell genomes proximal to centromeres and telomeres that are in the partially methylated mCG state, and that bear the heterochromatin modification H3K9me3, may often be resistant to complete reprogramming of non-CG methylation to the embryonic state, remaining in a somatic configuration after induction of pluripotency. As every iPSC methylome displayed a significantly lower non-CG methylation level averaged over this set of non-CG DMRs identified between H1 and ADS-iPSC genomes (exemplified for one DMR in FIG. 5 e), these regional aberrations appear to be a common and conserved feature of somatic cell reprogramming.

To determine if the non-CG mega-DMRs affected disruption of transcriptional activity, Applicants compared the transcript abundance between ADS-iPSCs and H1 ESCs of genes located within the ADS-iPSC non-CG mega-DMRs (FIG. 5 f). Of the 50 RefSeq genes within the non-CG mega-DMRs, 33 showed ≧2-fold lower transcript abundance in ADS-iPSC compared to H1 ESCs, while only 5 genes showed ≧2-fold higher transcript abundance in ADS-iPSC compared to H1 ESCs, and the changes were to a lesser degree (Supplementary table 7). This indicates that these aberrantly reprogrammed regions are associated with transcriptional disruption in the iPSCs (FIG. 5 g). Notably, 13 of the 15 CG DMRs that were consistently hypermethylated in every iPSC line (FIG. 4 d,e) were located within the non-CG mega-DMRs, constituting a highly significant enrichment (P=8.5e⁻³⁹). Finally, 64% of genes with lower transcript abundance in ADS-iPSC in non-CG mega-DMRs also displayed dense CG hypermethylation at the transcriptional start site (TSS, FIG. 5 f, red circles), a subset of which were consistently hypermethylated at the TSS in all iPSC lines analyzed and associated with aberrant loss of H3K27me3 (FIG. 5 f, blue circles, FIG. 4 e) providing potential molecular markers for determination of complete reprogramming in iPSC lines. Several of these suppressed genes exhibiting TSS CG-hypermethylation encode proteins that may be pertinent to processes of cellular differentiation and development, particularly in neural lineages: TMEM132D, implicated in panic/anxiety disorders in humans and mice (Erhardt, A. et al. Molecular psychiatry (2010)); FAM19A5, encoding a novel neuropeptide highly expressed in the CNS with neuroprotective roles (Yilmaz, G. et al., Exp Transl Stroke Med 2:11 (2010)); DPP6, encoding a protein associated with brain Kv4 channels and human neural disease (Clark, B. D. et al. Front Mol Neurosci 1:8 (2008)); TCERG1L, encoding a transcription factor enriched in the dorsal horn of the mouse embryonic spinal cord (Li, M. Z. et al. Dev Biol 292:555-564 (2006)); FZD10, encoding a Wnt protein receptor highly expressed in a variety of CNS tissues. Notably, three of these genes (TCERG1L, DPP6 and FAM19A5) were reported as consistently expressed significantly higher in ESCs compared to iPSCs (Chin, M. H. et al. Cell Stem Cell 5:111-123 (2009)) (J.A.T., personal communication), further indicating that the epigenomic dysregulation Applicants have characterized is a common feature of iPSCs. Applicants hypothesize that in the absence of reprogramming and differentiation methodologies that reset the aberrantly CG-hypermethylated and repressed state of these genes, derivation of fully functional neuronal lineages from iPSCs may be impeded. In this regard, it is noteworthy that a diminished frequency of neural differentiation of human iPSCs compared to human ESCs has previously been reported (Hu, B. Y. et al. Proc Natl Acad Sci USA 107:4335-4340 (2010)).

Through generation of the first unbiased, whole-genome, single-base resolution DNA methylomes for a variety of iPSCs and ESCs Applicants have gained several novel insights into the epigenomic reprogramming process. First, reprogramming induces a remarkable reconfiguration of the DNA methylation patterns throughout the somatic cell genome, returning PMDs to a fully methylated state, reinstating non-CG methylation, and reprogramming most unmethylated and methylated CGIs to an ESC-like state. Overall, this process generates an iPSC methylome that, in general, is very similar to that of ESCs. In addition, with new methylomes for both ESCs and somatic cells, the characteristics that Applicants previously proposed to demarcate a pluripotent DNA methylome from that of a differentiated cell remain applicable (Lister, R. et al. Nature 462:315-322 (2009)): non-CG methylation is a hallmark of pluripotent cells, while large tracts of partial CG methylation are characteristic of differentiated cells.

Upon closer inspection numerous aberrations in the reprogramming were evident, a significant fraction of which were present in all iPSC lines despite encompassing progenitor somatic cells from different germ layers and possessing different genotypes, reprogramming by independent laboratories, and using three different iPSC induction technologies. In terms of mCG, reprogramming generated hundreds of differentially methylated regions, most associated with CGIs and genes, and appearing to represent both memory of the somatic cell DNA methylation patterns as well as novel aberrant DNA methylation. Notably, many of the novel CG-DMRs were shared between independent iPSC lines, indicating that these loci are inherently susceptible to aberrant methylation in the reprogramming process. Furthermore, the presence of unique CG-DMRs in each iPSC line indicate that in addition to the aforementioned susceptible regions, there may be a stochastic element to reprogramming that results in inter-clone variability.

Applicants also identified megabase-scale genomic regions that were repeatedly resistant to reprogramming of non-CG methylation, and were associated with altered H3K9me3 and transcriptional activity, constituting phenotypic consequences at the transcriptional level that could have downstream consequences for iPSC or derived somatic cell function. The close proximity of the non-CG mega-DMRs to centromeres and telomeres suggests that there could be distinct molecular properties of these chromosomal regions, for example particular histone variants, which impede the reprogramming process. Together, the non-CG mega-DMRs, common CG-DMRs in all iPSC lines, and differentially expressed genes are useful as diagnostic markers for incomplete iPSC reprogramming, characterization of the efficacy of different reprogramming techniques, and potential propagation of altered methylation states into derivative differentiated cells. From these first comprehensive whole-genome, base-resolution methylome maps it appears clear that iPSCs are fundamentally distinct from ESCs, insofar as they manifest common, quantifiable epigenomic differences.

Example 4

Biological Materials and Sequencing Libraries.

Strand-specific mRNA-Seq libraries were produced as described previously (Lister, R. et al. Nature 462:315-322 (2009)). MethylC-Seq libraries were generated by ligation of methylated sequencing adapters to fragmented genomic DNA followed by purification, sodium bisulfite conversion and 4 cycles of PCR amplification as described previously (Lister, R. et al. Nature 462:315-322 (2009)), with minor modifications (See Supplementary Materials). ChIP-Seq libraries were prepared following Illumina protocols with minor modifications (See Supplementary Materials). Sequencing was performed using the Illumina Genome Analyzer IIx and HiSeq2000 instruments as per the manufacturer's instructions.

Read Processing and Alignment.

MethylC-Seq sequencing data was processed using the Illumina analysis pipeline and FastQ format reads were aligned to the human reference genome (hg18) using the Bowtie algorithm (Langmead, B. et al., Genome Biol. 10:R25 (2009)) as described previously (Lister, R. et al. Nature 462:315-322 (2009)), with minor modifications (See Supplementary Materials). mRNA-Seq reads were uniquely aligned to the human reference (hg18) and quantified using the TopHat36 and Cufflinks37 algorithms. Base calling, and mapping of Chip-Seq reads was performed using the Illumina analysis pipeline.

Cell Culture.

ADS cells were obtained from Invitrogen (Cat. #R7788110) and cultured under recommendation conditions. ADS cells were grown in 10 cm² dishes (5,000 cells/cm²). For making iPSC cells, ADS cells (3,000/cm²) were plated in six-well plates. The cells were infected with the combination of human reprogramming retroviruses (c-MYC, KLF4, OCT4, or SOX2 in pMXs; Addgene) that had been produced in 293T cells co-transfected with gag/pol and VSV-G as described above. On day 5, cells were passed onto 10-cm dishes covered with feeder MEFs or onto 6-cm dishes without MEFs. Cells were cultured in DMEM/F12 plus 20% KSR supplemented with β-mercaptoethanol (0.1%), NEAA (1×), Glutamax (1%), and 10 ng/mL FGF2. Medium was changed every day. On days 18-28, individual colonies were picked and cultured feeder-free in defined mTeSR1 medium on plates coated with matrigel. The profiled ADS-iPSC clone was assayed for pluripotency by analysis of the transcript abundance of pluripotency markers, and in vitro and in vivo (teratoma) differentiation into 3 germ layers, as described previously (Sugii, S. et al. Proceedings of the National Academy of Sciences (2010)). For differentiation from ADS cells to mature adipocyte in vitro, ADS cells (10,000/cm²) were plated on 10 cm² dishes with growth media. Differentiation was induced for 14 days using medium consisting of DMEM-F12, 10% KSR, and an adipogenic cocktail (0.5 mM IBMX, 0.25 uM dexamethasone, 1 ug/ml insulin, 0.2 mM indomethacin and 1 uM pioglitazone). For collecting mature adipocytes, the cells were detached with trypsin, then neutralized. After centrifuging detached cells, floated fat cells were transfer into new tubes. H9 cells were passage 42 including several passages in mTeSR1. IMR90-iPSCs were derived by lentiviral integration as reported previously (Yu, J. et al. Science 318:1917-1920 (2007)), and were passage 65, with 33 passages in mTeSR1. Foreskin fibroblast (FF) iPSC lines were derived using non-integrating episomal vectors as described previously (Yu, J. et al. Science 324:797-801 (2009)). Prior to cell harvest aliquots of cells were assayed for Oct4 expression by flow cytometry as described previously (Ludwig, T. et al. Nature Methods 3:637-646 (2006); Ludwig, T. et al. Nat Biotechnol 24:185-187 (2006)). These cells were submitted to the WiCell Cytogenetics Laboratory to confirm normal karyotype.

MethylC-Seq Library Generation.

Five μg of genomic DNA was extracted from frozen cell pellets using the DNeasy Mini Kit (Qiagen, Valencia, Calif.) and spiked with 25 ng unmethylated c1857 Sam7 Lambda DNA (Promega, Madison, Wis.). The DNA was fragmented with a Covaris S2 (Covaris, Woburn, Mass.) to 75-175 bp or 100-400 bp for single-read or paired-read libraries, respectively, followed by end repair and addition of a 3′ A base. Cytosine-methylated adapters provided by Illumina (Illumina, San Diego, Calif.) were ligated to the sonicated DNA as per manufacturer's instructions for genomic DNA library construction. For single-read libraries, adapter-ligated DNA was isolated by two rounds of purification with AMPure XP beads (Beckman Coulter Genomics, Danvers, Mass.). For paired-read libraries, adapter-ligated DNA of 275-375 bp (150-250 bp insert) was isolated by 2% agarose gel electrophoresis. Adapter-ligated DNA (≦450 ng) was subjected to sodium bisulfite conversion using the MethylCode kit (Life Technologies, Carlsbad, Calif.) as per manufacturer's instructions. The bisulfite-converted, adapter-ligated DNA molecules were enriched by 4-8 cycles of PCR with the following reaction composition: 2.5 U of uracil-insensitive PfuTurboC_(x) Hotstart DNA polymerase (Stratagene), 5 μl 10× PfuTurbo reaction buffer, 31 μM dNTPs, 1 μl Primer 1, 1 μl Primer 2 (50 μl final). The thermocycling parameters were: 95° C. 2 min, 98° C. 30 sec, then 4-8 cycles of 98° C. 15 sec, 60° C. 30 sec and 72° C. 4 min, ending with one 72° C. 10 min step. The reaction products were purified using AMPure XP beads. Up to two separate PCR reactions were performed on subsets of the adapter-ligated, bisulfite-converted DNA, yielding up to two independent libraries from the same biological sample. Final sequence coverage was obtained by sequencing all libraries for a sample separately, thus reducing the incidence of “clonal” reads which share the same alignment position and likely originate from the same template molecule in each PCR. The sodium bisulfite non-conversion rate was calculated as the percentage of cytosines sequenced at cytosine reference positions in the Lambda genome.

Directional RNA-Seq Library Generation.

Total RNA was isolated from cell pellets treated with RNAlater using the RNA mini kit (Qiagen, Valencia, Calif.) and treated with DNaseI (Qiagen, Valencia, Calif.) for 30 min at room temperature. Following ethanol precipitation, biotinylated LNA oligonucleotide rRNA probes complementary to the 5S, 5.8S, 12S, 18S and 28S ribosomal RNAs were used to deplete the rRNA from 5 μg of total RNA by RiboMinus (Life Technologies, Carlsbad, Calif.) as per manufacturer's instructions. Purified RNA (50 ng) was fragmented by metal hydrolysis in 1× fragmentation buffer (Life Technologies, Carlsbad, Calif.) for 15 min at 70° C., stopping the reaction by addition of 2 μl fragmentation stop solution (Life Technologies, Carlsbad, Calif.). Fragmented RNA was used to generate strand-specific RNA-seq libraries as per the Directional mRNA-seq Library Preparation Protocol (Illumina, San Diego, Calif.).

Chromatin Immunoprecipitation and ChIP-Seq Library Generation.

Chromatin immunoprecipitation (ChIP) and Illumina sequencing for H3K9me2 and H3K27me3 was performed as described previously (Hawkins, R. D. et al. Cell Stem Cell 6:479-491 (2010)).

Mapping Retroviral Insertion Sites.

MMLV retroviral insertion sites in ADS-iPSC genomic DNA were identified by an adapter ligation-mediated method for genome-wide mapping of insertions, as described previously (O'Malley, R. C. et al., Nat Protoc 2:2910-2917 (2007)), except with the following modifications. Genomic DNA was fragmented by sonication with a Covaris S2, followed by ligation of modified 5′ or 3′ LTR-specific Illumina adapters:

5′-LTR(5′-3′): CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGC TCTTCCGATCTTCAGTGCAGCTGTTCCATCTGTTCTTGGCCC 3′-LTR(5′-3′): CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGC TCTTCCGATCTTCAGTGGCCAGTCCTCCGATTGACTGAGTCG

A single mapping library was made each for the 5′ and 5′ LTRs, and each library was sequenced on the Illumina Genome Analyzer IIx. Each valid read contained the barcode sequence “TCAGTG” prepended to the 5′ of the genomic DNA read sequence. Retroviral insertion sites were identified by localized enrichment of greater than 300 reads within a 2 kb window, in both the 5′ LTR and 3′ LTR mapping libraries, and located on opposite genome strands between the two libraries. Cloning and Sanger sequencing of library molecules from the 3′ LTR mapping library confirmed genomic DNA-retroviral insertion sites for a representative fraction of the 17 insertion sites identified by high-throughput sequencing.

High-Throughput Sequencing.

Single-read methylC-seq and RNA-seq libraries were sequenced for up to 85 cycles using the Illumina Genome Analyzer IIx. Paired-read MethylC-seq libraries were sequenced for up to 75 cycles for each read using the Illumina HiSeq2000. Image analysis and base calling were performed with the standard Illumina pipeline, performing automated matrix and phasing calculations on a control library that was sequenced in a single lane of each flowcell.

Example 5

Processing and Alignment of MethylC-Seq Data to Identify Methylated Cytosines.

All sequence alignments were performed against the NCBI36/hg18 human reference. Single-read MethylC-seq sequences were processed and aligned as described previously (Lister, R. et al. Nature 462:315-322 (2009)), except an additional filter was added to remove any mapped reads in which a read-C base was aligned to a reference-T base. Paired-read MethylC-seq data was mapped and processed as described in previously (Lister, R. et al. Nature 462:315-322 (2009)) with the following modifications to accommodate the paired-read data-type. Both reads in a pair were trimmed of any low quality sequence at their 3′ ends and mapped to the reference genome with Bowtie v.0.12.5 (Langmead, B. et al., Genome Biol. 10:R25 (2009)) in paired-read mode, using the following parameters: -e 90 -l 20 -n 0 -k 10 -o 4 -I 0 -X 550 -pairtries 100 -nomaground -solexa1.3-quals. Mapped reads in a read pair that overlapped were trimmed from their respective 3′ ends until the reads no longer overlapped, leaving a 1 bp gap.

Mapped reads were filtered as follows: any read with more than 3 mismatches was trimmed from the 3′ end to contain 3 mismatches, any read pair which contained a cytosine mapped to a reference sequence thymine was removed, and any read pairs that had more than 3 cytosines in the non-CG context within a single read was removed (possible non-conversion in bisulfite reaction). Read pairs were then collapsed to remove clonal reads potentially produced in the PCR amplification from the same template molecule, based on common start position of read 1. The total uniquely-mapped, non-clonal read number for each library, average coverage and total sequence yield are detailed in Table S1.

For all MethylC-seq datasets, methylated cytosines were identified from the mapped and processed read data as described previously (Lister, R. et al. Nature 462:315-322 (2009)). The bisulfite conversion rates for all samples were over 99% (Table S1). Correction of any DNA methylation sites incorrectly categorized as non-CG due to SNPs in the sample versus reference genomes was performed as described previously (Lister, R. et al. Nature 462:315-322 (2009)).

Genome Annotation.

Genomic regions and CpG Islands (CGI) were defined based on NCBI BUILD 36/HG18 coordinates downloaded from UCSC web site. Promoters were arbitrarily defined as TSS+/−500 bp or 2000 bp for each Ref Seq transcript (as indicated in the text). According to the UCSC annotation many Ref Seq transcripts can be associated with a given gene, and they can have the same or alternative TSS. Gene bodies are defined as the transcribed regions, from the start to the end of transcription sites for each Ref Seq.

mC and Histone Profiles (FIG. 3-5).

FIG. 3: each CG-DMR was divided into 20 equally sized bins. The average methylation for all cytosines in the CG context within a bin in one sample was determined and normalized by the bin size. Finally, the whole dataset was divided by its 70th percentile, and values higher then 1 were forced to 1. This was performed to produce a meaningful mapping between values and colours in the heatmap key, and to avoid extreme values masking the methylation levels of other CG-DMRs. CG-DMRs were then reorganized based on their similarity by means of complete linkage hierarchical clustering, using the heatmap.2 R function.

FIG. 4 d: Each of the 15 CG-DMRs consistently hypermethylated in the 5 iPSC lines was profiled for both mCG and the H3K27me3 histone mark throughout the CG-DMR and equivalent upstream an downstream genomic regions divided into 30 equal length bins. For DNA methylation, for each bin in each sample the total number of methylated/(methylated+unmethylated) reads was determined over the whole set of considered CG-DMRs. Final profiles were normalized dividing them by their maximum value. For the H3K27me3 histone modification ChIP-Seq reads, RPKM values were determined in each CG-DMR and normalized to the average of the upstream/downstream flanking region RPKM values.

FIG. 5 b: As in FIG. 4 d lower axis, but based on the mC in the CH sequence context profiled over the non-CG mega-DMRs and upstream/downstream flanking regions, minus the non-conversion frequency. The final profiles were normalized to their maximum level.

FIG. 5 c: As in FIG. 4 d lower axis, but based on the mC in the CH sequence context profiled over non-CG mega-DMRs and upstream/downstream flanking regions minus the non-conversion frequency. In the upper axis the H3K9me3 histone modification ChIP-Seq reads were profiled as described for the H3K27me3 profiles in FIG. 4 d.

FIG. 5 d: As in FIG. 4 d lower axis, but based on the mC on the mCG sequence context profiled over non-CG mega-DMRs and upstream/downstream flanking regions. Profiles were normalized to their maximum levels.

FIG. 5 e: As in FIG. 4 d lower axis for one example non-CG mega-DMR using 10 kb bins.

Clustering of mC Profiles and Chromosome 10 Smoothed Profiles.

Methylation level for each C in the CG, CHG and CHH sequence context was summed in adjacent 10 kb windows over all autosomal chromosomes. Non-CG DNA methylation profiles were determined by adding mCHG and mCHH profiles. Clustering was performed based on the Pearson correlation over all 10 kb windows transformed into a distance measure (as 1-Pearson correlation) and using the hclust R function. Data for smoothing of non-CG mC on chromosome 10 were retrieved as for the clustering. In addition, smoothing with cubic splines was determined before plotting using the smooth.spline R function with spar argument set to 0.3.

Example 6

Identification of Differentially Methylated Regions (DMRs).

Non-CG Mega-DMRs.

Non-CG mega DMRs (FIG. 5) were identified comparing H1 to ADS-iPSC mCHG and mCHH smoothed methylation profiles. The average methylation level of mC called (1% FDR) in the mCHG and mCHH sequence context was determined in 5 kb windows (sW). The genome was scanned considering groups of 10 adjacent windows sW over a distance less than 50 kb. The set of 10 smoothed values for mCHG in the H1 sample was compared to the set of set of 10 smoothed values in the ADS-iPSC sample using the Wilcoxon test. Resulting P-values were corrected with the Benjamini-Hochberg method. Regions with P-value<0.01 (1% FDR) and 8 fold enrichment of methylation level were identified, and regions closer than 100 bp were joined. This was repeated for the mC in the CHH sequence context. Finally, mCHG and mCHH DMRs overlapping or closer than 100 kb were joined and the final set of regions was checked for having mCHG+mCHH fold enrichment of at least 2 fold between H1 and ADS-iPSC. Coverage in the region identified as differentially methylated was not significantly different between the samples or compared to flanking genomic regions, and was taken into account in the statistical test used for their identification. The set of 78 DMRs hypomethylated in ADS-iPSC (Supplementary FIG. 10 c-f) was further refined, considering the size and overlap with repressive histone marks. The final set of 22 regions reported in FIG. 5 includes all the DMRs larger than 1 Mb (17) and a range of smaller ones. Also, the 22 final non-CG mega-DMRs encompass ˜92% of the initial set of 78 DMRs, based on by size.

CG-DMRs.

CG-DMRs (FIG. 3) were identified similarly to non-CG mega-DMRs. Smoothed average methylation level was performed in 100 bp windows sW, and regions comprising a set of 10 adjacent windows sW over a distance less than 1100 bp were considered. The Kruskall-Wallis test was used to score each region based on the methylation levels from the two ESC and the five iPSC lines. Regions with P-value<0.01 (1% FDR) and 4 fold enrichment of methylation level (max/min over the 7 cell lines for each region) were identified, and regions closer than 100 bp were joined.

For the analysis of CGI reprogramming the CG-DMRs were identified as for the FIG. 3 CG-DMRs but including the IMR90, ADS-adipose and ADS differentiated cell lines in addition to the two ESC and the five iPSC lines.

CGI Reprogramming.

CG-DMRs different between ESCs and differentiated cells were defined within the set of CG-DMRs identified comparing all analyzed methylomes (see above), considering only CG-DMRs overlapping with CGI. In particular, for each of these CG-DMR the mCG/bp levels in 20 equally sized bins was profiled in all cell types. DMRs with pooled mCG/bp levels different from differentiated and ESC lines were identified (Wilcoxon test P-value<0.01). Similarly, the set of reprogrammed CG-DMRs was identified comparing pooled iPSC mCG profiles with the ESC samples (Wilcoxon test P-value>0.05).

CG-DMRs Reprogramming.

CG-DMRs aberrant in iPSCs and like or unlike parental cells were defined within the set of CG-DMRs identified comparing all ESC and iPSC samples. In particular, for each of these CG-DMR the mCG/bp levels in 20 equally sized bins was profiled in all cell types. ADS (or IMR90) aberrant CG-DMRs with pooled mCG/bp levels different between ADS-iPSC (or IMR90-iPSC) and ESC lines were identified (Wilcoxon test P-value<0.01). Similarly, the set of ADS (or IMR90) CG-DMRs unlike the parental line was identified comparing pooled ADS-iPSC (or IMR90-iPSC) mCG profiles with ADS (or IMR90) (Wilcoxon test P-value<0.01).

Consistency of iPSC reprogramming in many lines for each CG-DMR was determined by comparing the mCG pooled levels for each iPSC line compared to the ESC levels (Wilcoxon test) and counting how many iPSC lines had P-value<0.01.

Identification of Partially Methylated Domains (PMDs).

A sliding window approach was used to find regions of the genome that were partially methylated in each cell type, as described previously (Lister, R. et al. Nature 462:315-322 (2009)).

Mapping RNA-Seq Reads.

RNA-seq read sequences produced by the Illumina analysis pipeline were aligned with the TopHat software (Trapnell, C. et al., Bioinformatics 25:1105-1111 (2009)) to the NCBI BUILD 36/hg18 reference sequence. Reads that aligned to multiple positions were discarded. Reads per kilobase of transcript per million reads (RPKM) values were calculated with the Cufflinks software (Trapnell, C. et al. Nature Biotechnology 28:511-515 (2010)) using human RefSeq gene models.

Mapping and Enrichment Analysis of ChIP-Seq Reads.

Following sequencing cluster imaging, base calling and mapping were conducted using the Illumina pipeline. Clonal reads were removed from the total mapped tags, retaining only the non-clonal unique tags that mapped to one location in the genome, where each sequence is represented once. Regions of tag enrichment were identified as described previously (Hawkins, R. D. et al. Cell Stem Cell 6:479-491 (2010)).

Data Visualization in the AnnoJ Browser.

MethylC-Seq, RNA-seq and ChIP-Seq sequencing reads and positions of methylcytosines with respect to the NCBI BUILD 36/HG18 reference sequence, gene models and functional genomic elements were visualized in the AnnoJ 2.0 browser, as described previously (Lister, R. et al. Cell 133:523-536 (2008)). 

What is claimed is:
 1. A method of identifying a human induced pluripotent stem cell, said method comprising identifying a non-CpG hypomethylated DMR within said human induced pluripotent stem cell.
 2. The method of claim 1, wherein said non-CpG hypomethylated DMR is proximal to a telomere or centromere.
 3. The method of claim 1, wherein said non-CpG hypomethylated DMR comprises one or more hypermethylated promoters.
 4. The method of claim 1, wherein said non-CpG hypomethylated DMR substantially overlaps with a partially methylated domain of an origin cell of said reprogrammed human induced pluripotent stem cell.
 5. The method of claim 1, wherein said non-CpG hypomethylated DMR comprises one or more transcriptionally repressed genes.
 6. The method of claim 1, wherein said non-CpG hypomethylated DMR is spatially concordant with a H3K9me3 heterochromatin modification.
 7. The method of claim 1, wherein said method further comprises identifying one or more CG-DMRs within said reprogrammed human induced pluripotent stem cell.
 8. The method of claim 7, wherein said one or more CG-DMRs are hypomethylated CG-DMRs.
 9. The method of claim 7, wherein said one or more CG-DMRs are hypermethylated CG-DMRs.
 10. The method of claim 1, wherein said identifying is indicative of an aberrantly reprogrammed human induced pluripotent stem cell.
 11. The method of claim 1, wherein said human induced pluripotent stem cell is formed by sexual or asexual propagation of one or more parent reprogrammed human induced pluripotent stem cells.
 12. A method of identifying a human induced pluripotent stem cell, the method comprising identifying a hypomethylated CG-DMR, or a hypermethylated CG-DMR within said human induced pluripotent stem cell.
 13. The method of claim 12, wherein said CG-DMR is a hypermethylated CG-DMR.
 14. The method of claim 13, wherein said method further comprises identifying one or more non-CpG hypomethylated DMRs.
 15. A method of identifying a human induced pluripotent stem cell, comprising: (i) determining a methylation pattern of at least a portion of a subject cell; and (ii) comparing said methylation pattern to a human embryonic stem cell methylation pattern, wherein a difference in methylation pattern is indicative of said subject cell being a human induced pluripotent stem cell.
 16. The method of claim 15, wherein said methylation pattern of said portion of said subject cell comprises a non-CpG hypomethylated DMR.
 17. The method of claim 16, wherein said non-CpG hypomethylated DMR is proximal to a telomere or centromer.
 18. The method of claim 16, wherein said non-CpG hypomethylated DMR comprises one or more transcriptionally repressed genes.
 19. The method of claim 15, wherein said methylation pattern of said portion of said subject cell comprises a non-CpG hypomethylated DMR and a CG-DMR within said subject cell.
 20. The method of claim 15, wherein said subject cell is formed by sexual or asexual propagation of one or more parent reprogrammed human induced pluripotent stem cells. 